StochFrozenLake_QUANTUM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Author: Andrea Baldanza \n",
        "\n",
        "Contact: baldanzaandrea@gmail.com\n",
        "\n",
        "\n",
        "Internship work, Porto University, department of Physics.\n",
        "\n",
        "Technical report: \"Reinforcement learning: from principles to Variational Quantum Circuits implementation\"\n",
        "Link https://drive.google.com/file/d/1r5iE4CW0Xfa5onOq9pJunXFbgkU9CjRh/view?usp=sharin\n",
        "\n",
        "\n",
        "Supervisors: Duarte Magano and Ariel Guerreiro\n",
        "\n",
        "2024, July"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# VARIATIONAL QUANTUM CIRCUIT FOR REINFORCEMENT LEARNING (NEEDED IMPROVEMENTS) #"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ACCp5CE8cc33"
      },
      "source": [
        "# Import of libraries #"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Su2Z9um67DMY",
        "outputId": "6b5ab427-a9b2-4520-ae90-c6d012b6dcec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install qiskit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HKQTz-HgD8kf",
        "outputId": "d90ab116-ea23-4580-9d47-6cac6a84b615"
      },
      "outputs": [
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          "name": "stdout",
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          ]
        }
      ],
      "source": [
        "!pip install qiskit-aer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yDsUM1ux-DCn",
        "outputId": "6c928e95-8f7a-41f6-da9f-6cf04eb59539"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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          ]
        }
      ],
      "source": [
        "!pip install pennylane"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (2.3.1)\n",
            "Requirement already satisfied: filelock in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from torch) (3.15.4)\n",
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            "Requirement already satisfied: tbb==2021.* in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from mkl<=2021.4.0,>=2021.1.1->torch) (2021.13.0)\n",
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          ]
        }
      ],
      "source": [
        "!pip install torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "SD0j2xHwBq6i"
      },
      "outputs": [],
      "source": [
        "from qiskit.quantum_info import Statevector"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l08gv4rbcgOm",
        "outputId": "559a5884-d1e6-4591-c163-f08c42f34c47"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.3.1+cpu\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import random\n",
        "import os.path\n",
        "#from torchsummary import summary\n",
        "\n",
        "import torch\n",
        "#import torchvision\n",
        "print(torch.__version__)\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from torch import nn, optim\n",
        "import torch.nn.functional as F\n",
        "#from torchvision import datasets, transforms, models\n",
        "\n",
        "import os\n",
        "from PIL import Image\n",
        "#import torchvision.transforms as transforms\n",
        "\n",
        "#import torchvision.transforms as transforms\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "#import torchvision\n",
        "import torch.optim as optim\n",
        "from torch.optim.lr_scheduler import StepLR\n",
        "#import pennylane"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "WcEBxOiw6uRP"
      },
      "outputs": [],
      "source": [
        "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, transpile, assemble\n",
        "from qiskit_aer import Aer\n",
        "from qiskit.visualization import plot_histogram\n",
        "from qiskit import *\n",
        "from qiskit.quantum_info import *\n",
        "\n",
        "from qiskit import *\n",
        "from qiskit.visualization import *\n",
        "#from qiskit.tools import *\n",
        "\n",
        "from qiskit.visualization import circuit_drawer\n",
        "from qiskit.visualization import array_to_latex\n",
        "from qiskit.visualization import circuit_drawer\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQ5K8kAPovCK"
      },
      "source": [
        "# INITIALIZE VALUES #"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "tJjCWOGxovWO"
      },
      "outputs": [],
      "source": [
        "\n",
        "Nx=4  #Points on x coordinate\n",
        "\n",
        "Ny=3  #Points on y coordinate\n",
        "\n",
        "\n",
        "\n",
        "Positions_of_obstacles=np.array([[1,1]])\n",
        "\n",
        "\n",
        "ALIVE_position=[Nx-1,Ny-1]\n",
        "DEATH_position=[Nx-1,Ny-2]\n",
        "\n",
        "\n",
        "max_epoch = 100 #for Bellmann\n",
        "NMEANS=5\n",
        "r=-0.004\n",
        "Gamma=0.95\n",
        "N_times=1000\n",
        "num_episodes=30000\n",
        "epsilon_0=0.6\n",
        "epsilon_decay_steps = 1 #Every how much EPISODES divide by 2 epsilon\n",
        "max_step_epsilon_min = (num_episodes)*5/10  #step when epsilon becomes epsilon_min\n",
        "epsilon_min_SARSA = 0.0#0.0\n",
        "epsilon_min_Q =0.01#0.01\n",
        "alpha_0=0.2\n",
        "Decay_rate_max_step=5000 #Every how much EPISODES divide by 2 alpha\n",
        "alpha_min_SARSA = 0.01\n",
        "alpha_min_Q_LEARNING = 0.01\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MY6Hi6h_lGMS"
      },
      "source": [
        "# ENVIRONMENT #"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "yKpMLeZfZgTo"
      },
      "outputs": [],
      "source": [
        "Nx=4\n",
        "Ny=3\n",
        "ALIVE_positions = np.array([[3,2]])\n",
        "\n",
        "DEATH_positions = np.array([[3,1]])\n",
        "\n",
        "Obstacles_positions = np.array([[1,1]])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PpCe_TJZaLny",
        "outputId": "b7de300b-cbc0-434d-8428-d7cae7f70f2c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "if any([1,1]==Obstacle.tolist() for Obstacle in Obstacles_positions):\n",
        "  print(\"True\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7YIATuFibg0X"
      },
      "source": [
        "MAP x,y -> index and the contrary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "QOmuw4nbbeko"
      },
      "outputs": [],
      "source": [
        "def position_to_index(s):\n",
        "  x = s[0]\n",
        "  y = s[1]\n",
        "  index = x + Nx * y\n",
        "  return index\n",
        "\n",
        "def index_to_position(index):\n",
        "  x = index % Nx\n",
        "  y = int(index / Nx)\n",
        "  return np.array([x,y])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UhDWgtuRb7hq",
        "outputId": "65743373-a16c-45e4-aa46-e73e33489f10"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6\n",
            "[2 1]\n",
            "Alive indexes =  [11]\n",
            "DEATH indexes =  [7]\n",
            "OBSTACLES indexes =  [5]\n"
          ]
        }
      ],
      "source": [
        "print(position_to_index([2,1]))\n",
        "print(index_to_position(6))\n",
        "\n",
        "ALIVE_indexes = [position_to_index(element) for element in ALIVE_positions]\n",
        "DEATH_indexes = [position_to_index(element) for element in DEATH_positions]\n",
        "OBSTACLES_indexes = [position_to_index(element) for element in Obstacles_positions]\n",
        "\n",
        "print(\"Alive indexes = \",ALIVE_indexes)\n",
        "print(\"DEATH indexes = \",DEATH_indexes)\n",
        "print(\"OBSTACLES indexes = \",OBSTACLES_indexes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p0LwCMOVcW8D"
      },
      "source": [
        "Creating adiacent lists 0=up, 1=right, 2=down, 3=left. Neigh_dist = neighborhood distances"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tenPAY7scaD6",
        "outputId": "d7624ed2-00f9-434b-8237-debf396542f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[ 4.  1.  0.  0.]\n",
            " [ 0.  1.  0. -1.]\n",
            " [ 4.  1.  0. -1.]\n",
            " [ 4.  0.  0. -1.]\n",
            " [ 4.  0. -4.  0.]\n",
            " [ 0.  0.  0.  0.]\n",
            " [ 4.  1. -4.  0.]\n",
            " [ 0.  0.  0.  0.]\n",
            " [ 0.  1. -4.  0.]\n",
            " [ 0.  1.  0. -1.]\n",
            " [ 0.  1. -4. -1.]\n",
            " [ 0.  0.  0.  0.]]\n"
          ]
        }
      ],
      "source": [
        "neigh_dist = np.zeros((Nx*Ny,4))\n",
        "for index in range(Nx * Ny):\n",
        "  neigh_dist[index,0] = Nx\n",
        "  if index + Nx >= Nx*Ny or (index + Nx in OBSTACLES_indexes):\n",
        "    neigh_dist[index,0] = 0\n",
        "\n",
        "  neigh_dist[index,1] = +1\n",
        "  if (index + 1) % Nx == 0 or (index + 1 in OBSTACLES_indexes):\n",
        "    neigh_dist[index,1] = 0\n",
        "\n",
        "  neigh_dist[index,2] = -Nx\n",
        "  if (index - Nx)<0  or (index - Nx in OBSTACLES_indexes):\n",
        "    neigh_dist[index,2] = 0\n",
        "\n",
        "  neigh_dist[index,3] = -1\n",
        "  if (index)%Nx==0 or (index - 1 in OBSTACLES_indexes):\n",
        "    neigh_dist[index,3] = 0\n",
        "\n",
        "  if (index in OBSTACLES_indexes) or (index in ALIVE_indexes) or (index in DEATH_indexes):\n",
        "    for neigh in range(0,4):\n",
        "      neigh_dist[index,neigh] = 0\n",
        "\n",
        "\n",
        "print(neigh_dist)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "548Ro927g16P"
      },
      "source": [
        "From action to probability and the next position function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "s6dnGDsfg76R"
      },
      "outputs": [],
      "source": [
        "def prob_action(action):\n",
        "  '''\n",
        "  This function take an action in input,\n",
        "  give in output 2 arrays:\n",
        "  1) probability to go up(0),right (1), down(2), left(3) in an array  [P(0), P(1), P(2), P(3)]\n",
        "  2) Indexes of action [MOST_probable, SECOND_MOST_PROBABLE, THIRD_MOST_PROBABLE]\n",
        "  i.e. If you choose action 0 (go up), you will have as output [0.8, 0.1, 0.0, 0.1], [0,1,3] Because the most probable thing that our agent can do is\n",
        "  to go up (0) with a probability 0.8, the second is go right with probability 0.1 and the third is go left(3) with probability 0.1.\n",
        "  '''\n",
        "  if action==0: #up\n",
        "    output = [[0.8, 0.1 ,0.0, 0.1], [0,1,3]]\n",
        "    return output\n",
        "\n",
        "\n",
        "  if action==1: #right\n",
        "    output = [[0.1, 0.8 ,0.1, 0.0], [1,0,2]]\n",
        "    return output\n",
        "\n",
        "\n",
        "  if action==2: #down\n",
        "    output = [[0.0, 0.1 ,0.8, 0.1], [2,1,3]]\n",
        "    return output\n",
        "\n",
        "\n",
        "  if action==3: #left\n",
        "    output = [[0.1, 0.0 ,0.1, 0.8], [3,0,2]]\n",
        "    return output\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6FcdJzBh4Tq",
        "outputId": "88b33bca-9411-4f86-c0c1-89d489c0b54d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.1, 0.0, 0.1, 0.8] [3, 0, 2]\n",
            "[0.1, 0.0, 0.1, 0.8] [3, 0, 2]\n"
          ]
        }
      ],
      "source": [
        "print(prob_action(3)[0], prob_action(3)[1])\n",
        "a,b = prob_action(3)\n",
        "print(a,b)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "6l60YooBipFt"
      },
      "outputs": [],
      "source": [
        "def Next_position(initial,action, control=False): #This function works when the 2 less probable actions have same probability.\n",
        "  prob,action_indexes = prob_action(action)\n",
        "  rnd = np.random.rand()\n",
        "  if control:\n",
        "    print(rnd)\n",
        "  if rnd<prob[action_indexes[0]]:\n",
        "    final = initial + neigh_dist[initial, action_indexes[0]]\n",
        "\n",
        "\n",
        "  if rnd>prob[action_indexes[0]] and rnd<prob[action_indexes[0]]+prob[action_indexes[1]]:\n",
        "    final = initial + neigh_dist[initial, action_indexes[1]]\n",
        "\n",
        "  if rnd>prob[action_indexes[0]] + prob[action_indexes[1]]:\n",
        "    final = initial + neigh_dist[initial, action_indexes[2]]\n",
        "  if control:\n",
        "    print(final,prob[action_indexes[0]],rnd>prob[action_indexes[0]] and rnd<prob[action_indexes[0]]+prob[action_indexes[1]], prob[action_indexes[0]] + prob[action_indexes[1]])\n",
        "  return int(final)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6rQn51SFgLH",
        "outputId": "f27350f0-739e-463e-c66a-bf8831c35149"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[-0.004 -0.004 -0.004 -0.004 -0.004  0.    -0.004 -1.    -0.004 -0.004\n",
            " -0.004  1.   ]\n"
          ]
        }
      ],
      "source": [
        "R=np.zeros(Nx*Ny)\n",
        "for i in range(Nx*Ny):\n",
        "  if (i in ALIVE_indexes):\n",
        "    R[i] = +1.0\n",
        "  elif (i in DEATH_indexes):\n",
        "    R[i] = -1.0\n",
        "  else: R[i] = r\n",
        "\n",
        "  if (i in OBSTACLES_indexes):\n",
        "    R[i] = 0.0\n",
        "\n",
        "print(R)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9yIWhmlqdxL",
        "outputId": "8f4995d9-5f4f-4bc7-bc68-d927f5c710f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8997015612078311 0.8 4.0 0.8 0.9\n",
            "6\n"
          ]
        }
      ],
      "source": [
        "def Next_position(initial,action, control=False, stochastic = False): #This function works when the 2 less probable actions have same probability.\n",
        "  prob,action_indexes = prob_action(action)\n",
        "  rnd = np.random.rand()\n",
        "  if control:\n",
        "    print(rnd,prob[action_indexes[0]],neigh_dist[initial, action_indexes[0]],prob[action_indexes[0]], prob[action_indexes[0]] + prob[action_indexes[1]])\n",
        "  if rnd<prob[action_indexes[0]]:\n",
        "    final = initial + neigh_dist[initial, action_indexes[0]]\n",
        "\n",
        "  if rnd>prob[action_indexes[0]] and rnd<prob[action_indexes[0]] +prob[action_indexes[1]]:\n",
        "    final = initial + neigh_dist[initial, action_indexes[1]]\n",
        "\n",
        "\n",
        "  if rnd > prob[action_indexes[1]]+prob[action_indexes[0]]:\n",
        "    final = initial + neigh_dist[initial, action_indexes[2]]\n",
        "\n",
        "  if not stochastic:\n",
        "    final = initial + neigh_dist[initial,action_indexes[0]]\n",
        "\n",
        "  return int(final)\n",
        "\n",
        "print(Next_position(2,0,control = True))\n",
        "\n",
        "def random_state():\n",
        "  output = int(np.random.rand()*12)\n",
        "  while (output in OBSTACLES_indexes) or (output in ALIVE_indexes) or (output in DEATH_indexes):\n",
        "    output = int(np.random.rand()*12)\n",
        "  return output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RGc6O5YTtcC5"
      },
      "source": [
        "## Function of plot policy ##"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "a2N1-omrtel-",
        "outputId": "364773ce-f2c8-4397-9ebd-63f5643074f3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'\\npolicy = np.ones(12)\\npolicy[0] = 3\\n\\npolicy[1] = 1\\n\\npolicy[2] = 2\\nplot_policy(policy)\\nplt.show()\\n'"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "def plot_policy(policy, title =\"my_policy\", Alive_indexes = [11], Death_indexes = [7], Obstacles_indexes = [5], Nx = 4, Ny = 3):\n",
        "    '''\n",
        "    this function take a policy in input that has to be encoded in the following matter:\n",
        "    0,0 -> 0\n",
        "    1,0 -> 1\n",
        "    2,0 -> 2\n",
        "    3,0 -> 3\n",
        "    0,1 -> 4\n",
        "    1,1 -> 5\n",
        "    2,1 -> 6\n",
        "    3,1 -> 7\n",
        "    0,2 -> 8\n",
        "    1,2 -> 9\n",
        "    2,2 -> 10\n",
        "    3,2 -> 11\n",
        "\n",
        "    x(i) = i % Nx\n",
        "    y(i) = i / Nx\n",
        "\n",
        "    '''\n",
        "    #set lim\n",
        "    plt.figure(figsize = (6,5))\n",
        "    plt.xlim(-0.5,+Nx- 0.5)\n",
        "    plt.ylim(-0.5,Ny-0.5)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "\n",
        "    #build the grid\n",
        "    for i in range(0,Ny+1):\n",
        "        plt.axhline(i-0.5,color = \"gray\")\n",
        "\n",
        "    for i in range(0,Nx+1):\n",
        "        plt.axvline(i-0.5,color = \"gray\")\n",
        "\n",
        "    #plot obstacoles and winning / losing states\n",
        "    for i in Obstacles_indexes:\n",
        "        plt.plot(i%Nx , int(i/Nx), marker = \"s\", markersize = 60, color = \"black\")\n",
        "    for i in Alive_indexes:\n",
        "        plt.plot(i%Nx , int(i/Nx) , marker = \"*\", markersize = 60, color = \"green\")\n",
        "    for i in Death_indexes:\n",
        "        plt.plot(i%Nx , int(i/Nx) , marker = \"o\", markersize = 60, color = \"red\")\n",
        "\n",
        "    for i in range(len(policy)):\n",
        "        if (i not in Alive_indexes) and (i not in Death_indexes) and (i not in Obstacles_indexes):\n",
        "            plt.text(i%Nx-0.4, int(i/Nx)-0.15, str(r))\n",
        "\n",
        "            if policy[i] == 0: #up\n",
        "                plt.arrow(i%Nx, int(i/Nx)-0.3, 0, +0.6, head_width=0.1, head_length=0.1, fc='black', ec='black') #up\n",
        "\n",
        "            if policy[i] == 1: #right\n",
        "                plt.arrow(i%Nx-0.3, int(i/Nx), +0.6, +0.0, head_width=0.1, head_length=0.1, fc='black', ec='black') #up\n",
        "\n",
        "            if policy[i] == 2: #down\n",
        "                plt.arrow(i%Nx, int(i/Nx)+0.3, 0, -0.6, head_width=0.1, head_length=0.1, fc='black', ec='black') #up\n",
        "\n",
        "            if policy[i] == 3: #left\n",
        "                plt.arrow(i%Nx+0.3, int(i/Nx), -0.6, +0.0, head_width=0.1, head_length=0.1, fc='black', ec='black') #up\n",
        "    name_file = title + \".pdf\"\n",
        "    plt.title(title)\n",
        "    plt.savefig(\"plot_policy.pdf\")\n",
        "\n",
        "\n",
        "\n",
        "'''\n",
        "policy = np.ones(12)\n",
        "policy[0] = 3\n",
        "\n",
        "policy[1] = 1\n",
        "\n",
        "policy[2] = 2\n",
        "plot_policy(policy)\n",
        "plt.show()\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-mr3M8W6qk6B"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TRANSITION PROBABILITY FUNCTION ##"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "ZZHzpM339haC",
        "outputId": "278fa7f2-80df-4640-a440-8e4711b513cd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'\\nmy_sum=0.0\\nfor i_state in range(6,8):\\n  for action in range(0,4):\\n    my_sum=0.0\\n    for f_state in range(0,12):\\n\\n      #print(i_state,f_state, action,transition_probability(f_state,i_state,action))\\n      my_sum+=transition_probability(f_state,i_state,action)\\n    #print(my_sum)\\n'"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def transition_probability(s_1,s_0,a, verbose=False): #s_0 and s_1 have indices, a has index of the action\n",
        "  output = 0.0\n",
        "  action_probabilities, _ = prob_action(a)\n",
        "  if verbose:\n",
        "    for j in range(0,4):\n",
        "      print(s_1, s_0+int(neigh_dist[s_0,j]),s_0)\n",
        "  if s_1 ==s_0 + int(neigh_dist[s_0,0]):\n",
        "    if verbose:\n",
        "      print(\"Control 0\")\n",
        "    output += action_probabilities[0]\n",
        "\n",
        "  if s_1 ==s_0 + int(neigh_dist[s_0,1]):\n",
        "    if verbose:\n",
        "      print(\"Control 1\")\n",
        "    output += action_probabilities[1]\n",
        "\n",
        "  if s_1 ==s_0 + int(neigh_dist[s_0,2]):\n",
        "    if verbose:\n",
        "      print(\"Control 2\")\n",
        "    output += action_probabilities[2]\n",
        "\n",
        "  if s_1 ==s_0 + int(neigh_dist[s_0,3]):\n",
        "    if verbose:\n",
        "      print(\"Control 3\")\n",
        "    output += action_probabilities[3]\n",
        "\n",
        "  if (s_0 in ALIVE_indexes) or (s_0 in DEATH_indexes):\n",
        "    output=0.0\n",
        "  return output\n",
        "'''\n",
        "my_sum=0.0\n",
        "for i_state in range(6,8):\n",
        "  for action in range(0,4):\n",
        "    my_sum=0.0\n",
        "    for f_state in range(0,12):\n",
        "\n",
        "      #print(i_state,f_state, action,transition_probability(f_state,i_state,action))\n",
        "      my_sum+=transition_probability(f_state,i_state,action)\n",
        "    #print(my_sum)\n",
        "'''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eb-QaR4VEugw"
      },
      "source": [
        "# Bellmann update (THEORETICAL POLICY) #\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9npvNmsLE7eW",
        "outputId": "f5ee475e-ccb1-4289-d6b8-066f9acd7dca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 0.71087955  0.66205982  0.62375952  0.40934501  0.76901356  0.\n",
            "  0.68775684 -1.          0.82486972  0.89138264  0.95528939  1.        ]\n"
          ]
        }
      ],
      "source": [
        "U = np.zeros((12))\n",
        "U_1 = np.zeros((12))\n",
        "#U_1[11]=+1.0\n",
        "#U_1[7] = -1.0\n",
        "\n",
        "\n",
        "u_update = 0.0\n",
        "array_to_find_max = np.zeros(4)\n",
        "array_to_find_max2 = np.zeros(4)\n",
        "\n",
        "U_time = np.zeros((max_epoch,12))\n",
        "\n",
        "for epochs in range(max_epoch-1):\n",
        "  U=U_1.copy()\n",
        "  for state_i in range(12):\n",
        "\n",
        "    array_to_find_max = np.zeros(4)\n",
        "    for action in range(4):\n",
        "      for state_f in range(12):\n",
        "        array_to_find_max[action] += transition_probability(state_f,state_i,action) * (Gamma*U[state_f] + R[state_f])\n",
        "\n",
        "    u_update = np.max(array_to_find_max)\n",
        "\n",
        "    #if (state_i in OBSTACLES_indexes) or (state_i in ALIVE_indexes) or (state_i in DEATH_indexes):\n",
        "    #  u_update=R[state_i]\n",
        "    U_1[state_i] = u_update\n",
        "    U_time[epochs+1,state_i] = u_update\n",
        "U[11]=+1.0\n",
        "U[7] = -1.0\n",
        "print(U)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K0H6gFVBDwy0",
        "outputId": "dbb10363-7aa4-445c-d784-85fa24aaface"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0. 0. 0. 0.]\n"
          ]
        }
      ],
      "source": [
        " array_prova = np.zeros(4)\n",
        " for action in range(4):\n",
        "      for state_f in range(12):\n",
        "        array_prova[action] += transition_probability(state_f,11,action) * R[state_f]\n",
        "\n",
        "print(array_prova)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jpMbTu_OOtvM",
        "outputId": "9afcb862-0a2f-46d1-c9f8-bd9a43bf539d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 [0.75250479 0.67763717 0.70599757 0.71669295]\n",
            "0\n",
            "1 [0.66311176 0.63141958 0.66311176 0.7011156 ]\n",
            "3\n",
            "2 [0.65734595 0.45862764 0.6061481  0.66079949]\n",
            "3\n",
            "3 [-0.69668955  0.26841051  0.43078646  0.43994212]\n",
            "3\n",
            "4 [0.81369849 0.76878578 0.72250635 0.76878578]\n",
            "0\n",
            "5 [0. 0. 0. 0.]\n",
            "0\n",
            "6 [ 0.7330072  -0.64209511  0.4677833   0.70811036]\n",
            "0\n",
            "7 [0. 0. 0. 0.]\n",
            "0\n",
            "8 [0.83152101 0.87249444 0.78683609 0.8192841 ]\n",
            "1\n",
            "9 [0.89112202 0.94250804 0.89112202 0.8381723 ]\n",
            "1\n",
            "10 [0.95336978 0.96430462 0.73934373 0.87741073]\n",
            "1\n",
            "11 [0. 0. 0. 0.]\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "\n",
        "policy=np.zeros(12)\n",
        "for i in range(12):\n",
        "    my_sum=np.array([0.0,0.0,0.0,0.0])\n",
        "    for action in range(0,4):\n",
        "        for j in range(12):\n",
        "            my_sum[action]+=transition_probability(j,i,action)*U[j]\n",
        "    print(i, my_sum)\n",
        "    print(np.argmax(my_sum))\n",
        "    policy[i]=np.argmax(my_sum)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pRX3p0a1O-aW",
        "outputId": "443ac512-7b16-4b7a-f9bd-68b5b0696fd1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0. 3. 3. 3. 0. 0. 0. 0. 1. 1. 1. 0.]\n"
          ]
        }
      ],
      "source": [
        "print(policy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "5u3TNK-dQJhk"
      },
      "outputs": [],
      "source": [
        "\n",
        "def My_action(a):#convert the 0,1,2,3 to \"up\",\"right\",\"down\",left\n",
        "    if a==0: #UP\n",
        "        return \"up\"\n",
        "    elif a==1: #RIGHT\n",
        "        return \"right\"\n",
        "    elif a==2:#down\n",
        "        return \"down\"\n",
        "    elif a==3: #LEFT\n",
        "        return \"left\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "id": "1P3zvmXgPAus",
        "outputId": "fcff0ffc-48d2-4b3a-db49-0e2b23a85bfa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0.71087955 0.66205982 0.62375952 0.40934501 0.76901356 0.\n",
            " 0.68775684 0.         0.82486972 0.89138264 0.95528939 0.        ]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x1a14786c350>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "my_title = \"Bellmann_r=\" + f\"{r:,}\" + \",$\\gamma$=\" + f\"{Gamma:,}\"\n",
        "plot_policy(policy, title = my_title)\n",
        "plt.figure()\n",
        "for j in range(12):\n",
        "  if (j not in OBSTACLES_indexes) and (j not in ALIVE_indexes) and (j not in DEATH_indexes):\n",
        "    position_str = \"(\" + str(j%Nx) + \",\" + str(int(j/Nx)) + \")\"\n",
        "    plt.plot(U_time[:,j], label =position_str)\n",
        "\n",
        "U_final = U_time[max_epoch-1,:]\n",
        "print(U_final)\n",
        "plt.xlabel(\"number of steps\")\n",
        "plt.ylabel(\"U\")\n",
        "plt.title(\"Bellman convergences\")\n",
        "plt.savefig(\"Bellman convergences.pdf\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JUUxAeQxXTXa"
      },
      "source": [
        "# Hybrid quantum classical #\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANQjpmDWg4i5"
      },
      "source": [
        "## VARIATIONAL QUANTUM CIRCUIT ##"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "9psVqzXTklG6"
      },
      "outputs": [],
      "source": [
        "\n",
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "from pennylane.optimize import NesterovMomentumOptimizer\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "def decimalToBinaryFixLength(_length, _decimal):\n",
        "\tbinNum = bin(int(_decimal))[2:]\n",
        "\toutputNum = [int(item) for item in binNum]\n",
        "\tif len(outputNum) < _length:\n",
        "\t\toutputNum = np.concatenate((np.zeros((_length-len(outputNum),)),np.array(outputNum)))\n",
        "\telse:\n",
        "\t\toutputNum = np.array(outputNum)\n",
        "\treturn outputNum"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "EIL-mQ2KrfFh"
      },
      "outputs": [],
      "source": [
        "dtype = torch.DoubleTensor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "Uv7_4_kLrcif"
      },
      "outputs": [],
      "source": [
        "dev = qml.device('default.qubit', wires=4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "Hy9pwry_g6Tw"
      },
      "outputs": [],
      "source": [
        "\n",
        "@qml.qnode(dev, interface='torch')\n",
        "def circuit(weights, angles=None):\n",
        "\t\"\"\"The circuit of the variational classifier.\"\"\"\n",
        "\t# Can consider different expectation value\n",
        "\t# PauliX , PauliY , PauliZ , Identity\n",
        "\tfor W in weights:\n",
        "\t\tstatepreparation(angles)\n",
        "\t\tlayer(W)\n",
        "\treturn [qml.expval(qml.PauliZ(ind)) for ind in range(4)]\n",
        "\n",
        "\n",
        "def variational_classifier(var_Q_circuit,angles = None):\n",
        "\tweights = var_Q_circuit\n",
        "\t#print(\"WEIGHTS\", weights)\n",
        "\t#print()\n",
        "\t#print()\n",
        "\t#raw_output=torch.tensor(circuit(weights,angles=angles),requires_grad=True).type(dtype) * 2.0-1.0\n",
        "\traw_output=circuit(weights,angles=angles)\n",
        "\tfor i in range(len(raw_output)):\n",
        "\t\traw_output[i]=raw_output[i]*3.0\n",
        "\t#print(raw_output)\n",
        "\treturn(raw_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "7zZXHXeUlSk8"
      },
      "outputs": [],
      "source": [
        "def statepreparation(a):\n",
        "\n",
        "\t\"\"\"Quantum circuit to encode a the input vector into variational params\n",
        "\n",
        "\tArgs:\n",
        "\t\ta: feature vector of rad and rad_square => np.array([rad_X_0, rad_X_1, rad_square_X_0, rad_square_X_1])\n",
        "\t\"\"\"\n",
        "\n",
        "\t# Rot to computational basis encoding\n",
        "\t# a = [a_0, a_1, a_2, a_3, a_4, a_5, a_6, a_7, a_8]\n",
        "\n",
        "\tfor ind in range(len(a)):\n",
        "\t\tqml.RX(np.pi * a[ind], wires=ind)\n",
        "\t\tqml.RZ(np.pi * a[ind], wires=ind)\n",
        "\n",
        "\n",
        "def layer(W):\n",
        "\t\"\"\" Single layer of the variational classifier.\n",
        "\n",
        "\tArgs:\n",
        "\t\tW (array[float]): 2-d array of variables for one layer\n",
        "\t\"\"\"\n",
        "\n",
        "\tqml.CNOT(wires=[0, 1])\n",
        "\tqml.CNOT(wires=[1, 2])\n",
        "\tqml.CNOT(wires=[2, 3])\n",
        "\n",
        "\n",
        "\tqml.Rot(W[0, 0], W[0, 1], W[0, 2], wires=0)\n",
        "\tqml.Rot(W[1, 0], W[1, 1], W[1, 2], wires=1)\n",
        "\tqml.Rot(W[2, 0], W[2, 1], W[2, 2], wires=2)\n",
        "\tqml.Rot(W[3, 0], W[3, 1], W[3, 2], wires=3)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "nOe1d4UqiZXo"
      },
      "outputs": [],
      "source": [
        "def epsilon_greedy(var_Q_circuit, epsilon, n_action, s, train=False):\n",
        "  if train or np.random.rand()< ((epsilon)):\n",
        "    action = torch.argmax(torch.tensor(variational_classifier(var_Q_circuit = var_Q_circuit.clone().detach(), angles = decimalToBinaryFixLength(4,s))))\n",
        "    action = action.item()\n",
        "  else:\n",
        "    # need to be torch tensor\n",
        "    action = np.random.randint(0, 4)\n",
        "  return action"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGiIKXt8qIuK"
      },
      "source": [
        "## Training Loop ##"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "HDB39MJlrN1e"
      },
      "outputs": [],
      "source": [
        "def sample_random_batch(D,batch_len):\n",
        "  output = []\n",
        "  for i in range(batch_len):\n",
        "    random_index = int(np.random.rand()*len(D))\n",
        "    output.append(D[random_index])\n",
        "  return output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "mVsEv8R57Koh"
      },
      "outputs": [],
      "source": [
        "\n",
        "loss_fnc = nn.MSELoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "uY2Zu-p7lJGD"
      },
      "outputs": [],
      "source": [
        "dtype = torch.DoubleTensor\n",
        "\n",
        "## Define a FOUR qubit system\n",
        "dev = qml.device('default.qubit', wires=4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7X70KxV4qNVu",
        "outputId": "a8431a8c-75a5-44db-fe4f-3f86167ff87f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TEST\n",
            "tensor([[[ 0.1976, -0.4606,  0.0777],\n",
            "         [ 0.1888, -0.5475,  0.5672],\n",
            "         [-0.5354, -0.3602, -0.0691],\n",
            "         [ 0.2051, -0.1222,  0.0309]],\n",
            "\n",
            "        [[-0.0896, -0.4574,  0.0929],\n",
            "         [-0.0407, -0.1834, -0.1007],\n",
            "         [ 0.2416,  0.7841, -0.0297],\n",
            "         [-0.1962, -0.1004,  0.3078]]], dtype=torch.float64,\n",
            "       requires_grad=True)\n",
            "[tensor(2.1561, dtype=torch.float64, grad_fn=<MulBackward0>), tensor(-2.1696, dtype=torch.float64, grad_fn=<MulBackward0>), tensor(1.4334, dtype=torch.float64, grad_fn=<MulBackward0>), tensor(-2.0847, dtype=torch.float64, grad_fn=<MulBackward0>)]\n",
            "\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-0.46,0.08)──RX(3.14)──────────────RZ(3.14)──────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-0.55,0.57)──RX(0.00)──────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●─────────────────────Rot(-0.54,-0.36,-0.07)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X─────────────────────Rot(0.21,-0.12,0.03)──\n",
            "\n",
            "────────────╭●─────────Rot(-0.09,-0.46,0.09)────────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-0.04,-0.18,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●───────────────────────Rot(0.24,0.78,-0.03)──┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X───────────────────────Rot(-0.20,-0.10,0.31)─┤  <Z>\n",
            "39\n",
            "Ustep of lr2: 0.2\n",
            "5.328194991826416\n",
            "42\n",
            "Ustep of lr2: 0.2\n",
            "27\n",
            "Ustep of lr2: 0.2\n",
            "21\n",
            "Ustep of lr2: 0.1\n",
            "12\n",
            "Ustep of lr2: 0.1\n",
            "1\n",
            "Ustep of lr2: 0.1\n",
            "8\n",
            "Ustep of lr2: 0.05\n",
            "94\n",
            "Ustep of lr2: 0.05\n",
            "58\n",
            "Ustep of lr2: 0.05\n",
            "7\n",
            "Ustep of lr2: 0.025\n",
            "89\n",
            "Ustep of lr2: 0.025\n",
            "74\n",
            "Ustep of lr2: 0.025\n",
            "42\n",
            "Ustep of lr2: 0.0125\n",
            "38\n",
            "Ustep of lr2: 0.0125\n",
            "193\n",
            "Ustep of lr2: 0.0125\n",
            "12\n",
            "Ustep of lr2: 0.00625\n",
            "190\n",
            "Ustep of lr2: 0.00625\n",
            "55\n",
            "Ustep of lr2: 0.00625\n",
            "2\n",
            "Ustep of lr2: 0.003125\n",
            "142\n",
            "Ustep of lr2: 0.003125\n",
            "41\n",
            "Ustep of lr2: 0.003125\n",
            "GOOD Q (left to the green): 1.460792391870276 , BAD Q (under the red) -0.8002585576848489 , BAD Q 2(left of the red) 1.6689966439587578\n",
            "GOOD V (left to the green): 1.5724076208812936 , BAD V (under the red) 1.6689966439587578\n",
            "MSE_value= tensor(0.7178, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-0.96,0.37)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-7.30,-2.25)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.01,3.14)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.71,-2.10)\n",
            "\n",
            "────────────╭●─────────Rot(0.04,-1.36,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.08,1.46,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.00,6.85,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(2.08,-5.76,0.31)──┤  <Z>\n",
            "Learning rate: 0.0015625 MSE= tensor(0.7178, dtype=torch.float64)\n",
            "GAME = 20, LOSSavg20 = 5.32819499, number of steps done in last= 41, last_episode's_last_state = 7, epsilon = 0.9395, game_rewardavg20 = 0.0738\n",
            "1\n",
            "Ustep of lr2: 0.0015625\n",
            "39\n",
            "Ustep of lr2: 0.0015625\n",
            "78\n",
            "Ustep of lr2: 0.0015625\n",
            "147\n",
            "1\n",
            "160\n",
            "1\n",
            "2\n",
            "21\n",
            "213\n",
            "24\n",
            "3\n",
            "1\n",
            "1\n",
            "3\n",
            "74\n",
            "71\n",
            "1\n",
            "2\n",
            "1\n",
            "GOOD Q (left to the green): 1.303545638283401 , BAD Q (under the red) -0.9798038521204476 , BAD Q 2(left of the red) 1.2945395204428425\n",
            "GOOD V (left to the green): 1.303545638283401 , BAD V (under the red) 1.2945395204428425\n",
            "MSE_value= tensor(0.3451, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.45)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-7.21,-2.23)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-0.93,3.36)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.72,-2.08)\n",
            "\n",
            "────────────╭●─────────Rot(-0.08,-1.46,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.09,1.58,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.04,6.80,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.12,-5.72,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.3451, dtype=torch.float64)\n",
            "GAME = 40, LOSSavg20 = 1.70792379, number of steps done in last= 1, last_episode's_last_state = 11, epsilon = 0.9295, game_rewardavg20 = -0.1763\n",
            "19\n",
            "4\n",
            "131\n",
            "28\n",
            "1\n",
            "155\n",
            "12\n",
            "30\n",
            "196\n",
            "79\n",
            "65\n",
            "1\n",
            "1\n",
            "38\n",
            "15\n",
            "57\n",
            "2\n",
            "1\n",
            "1\n",
            "95\n",
            "GOOD Q (left to the green): 1.0625745134898192 , BAD Q (under the red) -0.9672729595594634 , BAD Q 2(left of the red) 1.012492820363991\n",
            "GOOD V (left to the green): 1.0625745134898192 , BAD V (under the red) 1.012492820363991\n",
            "MSE_value= tensor(0.1421, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.07,0.47)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-7.17,-2.16)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-0.88,3.56)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.64,-2.09)\n",
            "\n",
            "────────────╭●─────────Rot(-0.12,-1.50,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.16,1.60,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.13,6.71,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.20,-5.60,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1421, dtype=torch.float64)\n",
            "GAME = 60, LOSSavg20 = 0.92239254, number of steps done in last= 95, last_episode's_last_state = 7, epsilon = 0.9195, game_rewardavg20 = -0.0123\n",
            "1\n",
            "3\n",
            "1\n",
            "57\n",
            "1\n",
            "109\n",
            "1\n",
            "3\n",
            "81\n",
            "1\n",
            "1\n",
            "1\n",
            "175\n",
            "1\n",
            "24\n",
            "63\n",
            "18\n",
            "1\n",
            "1\n",
            "1\n",
            "GOOD Q (left to the green): 0.9722387081377872 , BAD Q (under the red) -0.9927431204565587 , BAD Q 2(left of the red) 0.8520115444183131\n",
            "GOOD V (left to the green): 0.9722387081377872 , BAD V (under the red) 0.8520115444183131\n",
            "MSE_value= tensor(0.0896, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.07,0.45)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-7.13,-2.16)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-0.84,3.65)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-2.09)\n",
            "\n",
            "────────────╭●─────────Rot(-0.15,-1.54,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.18,1.63,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.16,6.68,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.22,-5.58,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0896, dtype=torch.float64)\n",
            "GAME = 80, LOSSavg20 = 0.63047775, number of steps done in last= 1, last_episode's_last_state = 11, epsilon = 0.9095, game_rewardavg20 = -0.0127\n",
            "58\n",
            "3\n",
            "3\n",
            "17\n",
            "43\n",
            "40\n",
            "1\n",
            "25\n",
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            "250\n",
            "201\n",
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            "197\n",
            "6\n",
            "1\n",
            "73\n",
            "3\n",
            "1\n",
            "1\n",
            "GOOD Q (left to the green): 0.8892117427762637 , BAD Q (under the red) -0.8102442666775886 , BAD Q 2(left of the red) 0.6427582407290773\n",
            "GOOD V (left to the green): 0.8892117427762637 , BAD V (under the red) 0.6427582407290773\n",
            "MSE_value= tensor(0.0732, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.05,0.51)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-7.06,-2.14)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-0.83,3.82)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-2.03)\n",
            "\n",
            "────────────╭●─────────Rot(-0.12,-1.51,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.17,1.69,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.25,6.52,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.20,-5.55,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0732, dtype=torch.float64)\n",
            "GAME = 100, LOSSavg20 = 0.48760036, number of steps done in last= 1, last_episode's_last_state = 7, epsilon = 0.8995, game_rewardavg20 = 0.0681\n",
            "46\n",
            "61\n",
            "3\n",
            "250\n",
            "2\n",
            "63\n",
            "1\n",
            "3\n",
            "1\n",
            "3\n",
            "1\n",
            "130\n",
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            "22\n",
            "109\n",
            "6\n",
            "2\n",
            "61\n",
            "5\n",
            "3\n",
            "GOOD Q (left to the green): 0.9747597189938866 , BAD Q (under the red) -0.39765772310818115 , BAD Q 2(left of the red) 0.6531487389589904\n",
            "GOOD V (left to the green): 0.9747597189938866 , BAD V (under the red) 0.6833219981263088\n",
            "MSE_value= tensor(0.0650, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.00,0.60)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-7.00,-2.05)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.00,3.83)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.62,-1.99)\n",
            "\n",
            "────────────╭●─────────Rot(-0.03,-1.42,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.19,1.70,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.21,6.24,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.15,-5.55,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0650, dtype=torch.float64)\n",
            "GAME = 120, LOSSavg20 = 0.39845285, number of steps done in last= 3, last_episode's_last_state = 11, epsilon = 0.8895, game_rewardavg20 = 0.0923\n",
            "5\n",
            "1\n",
            "3\n",
            "1\n",
            "1\n",
            "1\n",
            "18\n",
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            "1\n",
            "14\n",
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            "3\n",
            "146\n",
            "2\n",
            "6\n",
            "1\n",
            "13\n",
            "76\n",
            "GOOD Q (left to the green): 0.961109387107028 , BAD Q (under the red) -0.29648947565248884 , BAD Q 2(left of the red) 0.5867386125755576\n",
            "GOOD V (left to the green): 0.961109387107028 , BAD V (under the red) 0.6636699661070216\n",
            "MSE_value= tensor(0.0766, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.02,0.63)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.97,-2.04)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.03,3.85)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.64,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(-0.00,-1.39,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.17,1.72,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.36,6.18,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.12,-5.56,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0766, dtype=torch.float64)\n",
            "GAME = 140, LOSSavg20 = 0.33729321, number of steps done in last= 76, last_episode's_last_state = 7, epsilon = 0.8795, game_rewardavg20 = 0.1060\n",
            "81\n",
            "66\n",
            "1\n",
            "150\n",
            "72\n",
            "33\n",
            "45\n",
            "1\n",
            "2\n",
            "4\n",
            "83\n",
            "3\n",
            "67\n",
            "1\n",
            "9\n",
            "34\n",
            "20\n",
            "14\n",
            "2\n",
            "2\n",
            "GOOD Q (left to the green): 0.9646273114604539 , BAD Q (under the red) -0.26843594012128363 , BAD Q 2(left of the red) 0.5477315167784613\n",
            "GOOD V (left to the green): 0.9646273114604539 , BAD V (under the red) 0.5477315167784613\n",
            "MSE_value= tensor(0.0869, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.64)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.96,-2.04)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.10,3.89)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.64,-1.92)\n",
            "\n",
            "────────────╭●─────────Rot(0.01,-1.37,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.15,1.75,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.52,6.29,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(2.08,-5.56,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0869, dtype=torch.float64)\n",
            "GAME = 160, LOSSavg20 = 0.29298008, number of steps done in last= 2, last_episode's_last_state = 7, epsilon = 0.8695, game_rewardavg20 = 0.0742\n",
            "1\n",
            "22\n",
            "4\n",
            "81\n",
            "3\n",
            "57\n",
            "13\n",
            "9\n",
            "1\n",
            "2\n",
            "1\n",
            "39\n",
            "2\n",
            "1\n",
            "1\n",
            "1\n",
            "1\n",
            "1\n",
            "2\n",
            "5\n",
            "GOOD Q (left to the green): 0.9496285455902382 , BAD Q (under the red) -0.3523157863016464 , BAD Q 2(left of the red) 0.4994965611711739\n",
            "GOOD V (left to the green): 0.9496285455902382 , BAD V (under the red) 0.4994965611711739\n",
            "MSE_value= tensor(0.0998, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.06,0.63)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.97,-2.06)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.11,3.93)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.62,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.00,-1.39,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.15,1.77,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.50,6.29,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(2.09,-5.54,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0998, dtype=torch.float64)\n",
            "GAME = 180, LOSSavg20 = 0.25857593, number of steps done in last= 5, last_episode's_last_state = 11, epsilon = 0.8595, game_rewardavg20 = 0.0625\n",
            "55\n",
            "2\n",
            "9\n",
            "82\n",
            "8\n",
            "1\n",
            "46\n",
            "91\n",
            "5\n",
            "3\n",
            "67\n",
            "50\n",
            "23\n",
            "106\n",
            "1\n",
            "29\n",
            "2\n",
            "55\n",
            "1\n",
            "4\n",
            "GOOD Q (left to the green): 0.9440481092843642 , BAD Q (under the red) -0.44503306568943196 , BAD Q 2(left of the red) 0.41951249075538244\n",
            "GOOD V (left to the green): 0.9440481092843642 , BAD V (under the red) 0.41951249075538244\n",
            "MSE_value= tensor(0.1403, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.10,0.62)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.96,-2.09)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.16,3.98)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.62,-1.91)\n",
            "\n",
            "────────────╭●─────────Rot(-0.01,-1.40,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.10,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.64,6.28,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.06,-5.54,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1403, dtype=torch.float64)\n",
            "GAME = 200, LOSSavg20 = 0.23085953, number of steps done in last= 4, last_episode's_last_state = 11, epsilon = 0.8495, game_rewardavg20 = 0.0680\n",
            "1\n",
            "4\n",
            "31\n",
            "11\n",
            "12\n",
            "46\n",
            "6\n",
            "2\n",
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            "3\n",
            "18\n",
            "28\n",
            "42\n",
            "1\n",
            "3\n",
            "17\n",
            "19\n",
            "69\n",
            "5\n",
            "10\n",
            "GOOD Q (left to the green): 0.922588057306504 , BAD Q (under the red) -0.5152899510581161 , BAD Q 2(left of the red) 0.38235901406358475\n",
            "GOOD V (left to the green): 0.922588057306504 , BAD V (under the red) 0.3834327565594683\n",
            "MSE_value= tensor(0.1507, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.12,0.60)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.97,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.18,4.01)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.60,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(-0.02,-1.41,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.10,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.60,6.29,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.08,-5.52,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1507, dtype=torch.float64)\n",
            "GAME = 220, LOSSavg20 = 0.20935032, number of steps done in last= 10, last_episode's_last_state = 7, epsilon = 0.8395, game_rewardavg20 = 0.0712\n",
            "1\n",
            "17\n",
            "5\n",
            "8\n",
            "2\n",
            "12\n",
            "55\n",
            "20\n",
            "23\n",
            "2\n",
            "4\n",
            "3\n",
            "29\n",
            "13\n",
            "5\n",
            "1\n",
            "15\n",
            "14\n",
            "64\n",
            "2\n",
            "GOOD Q (left to the green): 0.9135769691946731 , BAD Q (under the red) -0.6040965597961749 , BAD Q 2(left of the red) 0.35339223455622704\n",
            "GOOD V (left to the green): 0.9135769691946731 , BAD V (under the red) 0.35339223455622704\n",
            "MSE_value= tensor(0.1603, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.15,0.59)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.99,-2.12)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.20,4.03)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.55,-1.97)\n",
            "\n",
            "────────────╭●─────────Rot(-0.04,-1.43,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.11,1.87,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.60,6.28,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.12,-5.47,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1603, dtype=torch.float64)\n",
            "GAME = 240, LOSSavg20 = 0.19107781, number of steps done in last= 2, last_episode's_last_state = 7, epsilon = 0.8295, game_rewardavg20 = 0.0568\n",
            "2\n",
            "7\n",
            "1\n",
            "15\n",
            "23\n",
            "17\n",
            "38\n",
            "10\n",
            "2\n",
            "22\n",
            "77\n",
            "8\n",
            "3\n",
            "47\n",
            "44\n",
            "6\n",
            "4\n",
            "1\n",
            "241\n",
            "66\n",
            "GOOD Q (left to the green): 0.9588050863575781 , BAD Q (under the red) -0.6626392717317339 , BAD Q 2(left of the red) 0.29414285311308164\n",
            "GOOD V (left to the green): 0.9588050863575781 , BAD V (under the red) 0.30437491597773214\n",
            "MSE_value= tensor(0.1787, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.15,0.59)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.97,-2.14)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.24,4.04)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.56,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(-0.07,-1.45,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.07,1.93,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.46,6.30,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.10,-5.48,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1787, dtype=torch.float64)\n",
            "GAME = 260, LOSSavg20 = 0.17600873, number of steps done in last= 66, last_episode's_last_state = 7, epsilon = 0.8195, game_rewardavg20 = 0.0377\n",
            "1\n",
            "78\n",
            "1\n",
            "2\n",
            "1\n",
            "4\n",
            "2\n",
            "15\n",
            "19\n",
            "32\n",
            "2\n",
            "24\n",
            "14\n",
            "15\n",
            "122\n",
            "38\n",
            "5\n",
            "29\n",
            "1\n",
            "24\n",
            "GOOD Q (left to the green): 0.9128523614321319 , BAD Q (under the red) -0.6314596208691021 , BAD Q 2(left of the red) 0.2699047945593265\n",
            "GOOD V (left to the green): 0.9128523614321319 , BAD V (under the red) 0.2699144116583911\n",
            "MSE_value= tensor(0.1828, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.15,0.59)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.97,-2.13)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.27,4.08)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.56,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(-0.06,-1.44,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.09,1.92,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.42,6.30,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.09,-5.49,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1828, dtype=torch.float64)\n",
            "GAME = 280, LOSSavg20 = 0.16377465, number of steps done in last= 24, last_episode's_last_state = 7, epsilon = 0.8095, game_rewardavg20 = 0.0248\n",
            "62\n",
            "3\n",
            "8\n",
            "24\n",
            "38\n",
            "38\n",
            "5\n",
            "50\n",
            "1\n",
            "4\n",
            "114\n",
            "4\n",
            "57\n",
            "12\n",
            "61\n",
            "3\n",
            "4\n",
            "55\n",
            "3\n",
            "45\n",
            "GOOD Q (left to the green): 0.9379986703603929 , BAD Q (under the red) -0.5124707010999391 , BAD Q 2(left of the red) 0.23829205331240677\n",
            "GOOD V (left to the green): 0.9379986703603929 , BAD V (under the red) 0.23829205331240677\n",
            "MSE_value= tensor(0.1914, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.62)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.95,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.33,4.08)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.59,-1.91)\n",
            "\n",
            "────────────╭●─────────Rot(-0.03,-1.42,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.09,1.93,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.35,6.29,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.05,-5.51,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1914, dtype=torch.float64)\n",
            "GAME = 300, LOSSavg20 = 0.15271608, number of steps done in last= 45, last_episode's_last_state = 7, epsilon = 0.7995, game_rewardavg20 = 0.0291\n",
            "3\n",
            "16\n",
            "3\n",
            "7\n",
            "87\n",
            "4\n",
            "10\n",
            "2\n",
            "4\n",
            "29\n",
            "10\n",
            "2\n",
            "24\n",
            "24\n",
            "38\n",
            "16\n",
            "3\n",
            "74\n",
            "36\n",
            "13\n",
            "GOOD Q (left to the green): 0.9149150023951896 , BAD Q (under the red) -0.5116019902749082 , BAD Q 2(left of the red) 0.21680552517843754\n",
            "GOOD V (left to the green): 0.9149150023951896 , BAD V (under the red) 0.22354483883215975\n",
            "MSE_value= tensor(0.1923, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.61)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.95,-2.10)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.34,4.11)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.60,-1.89)\n",
            "\n",
            "────────────╭●─────────Rot(-0.04,-1.42,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.10,1.93,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.28,6.31,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.03,-5.52,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1923, dtype=torch.float64)\n",
            "GAME = 320, LOSSavg20 = 0.14384699, number of steps done in last= 13, last_episode's_last_state = 11, epsilon = 0.7895, game_rewardavg20 = 0.0458\n",
            "4\n",
            "13\n",
            "11\n",
            "1\n",
            "40\n",
            "3\n",
            "47\n",
            "2\n",
            "2\n",
            "3\n",
            "93\n",
            "1\n",
            "31\n",
            "3\n",
            "42\n",
            "40\n",
            "52\n",
            "31\n",
            "7\n",
            "1\n",
            "GOOD Q (left to the green): 0.9371631265359376 , BAD Q (under the red) -0.42322426815657765 , BAD Q 2(left of the red) 0.20541941340543524\n",
            "GOOD V (left to the green): 0.9371631265359376 , BAD V (under the red) 0.20707556727628362\n",
            "MSE_value= tensor(0.1988, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.12,0.63)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.08)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,4.10)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.60,-1.89)\n",
            "\n",
            "────────────╭●─────────Rot(-0.02,-1.40,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.11,1.93,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(-0.24,6.30,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(2.03,-5.52,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1988, dtype=torch.float64)\n",
            "GAME = 340, LOSSavg20 = 0.13568284, number of steps done in last= 1, last_episode's_last_state = 11, epsilon = 0.7795, game_rewardavg20 = 0.0511\n",
            "95\n",
            "19\n",
            "3\n",
            "13\n",
            "5\n",
            "72\n",
            "47\n",
            "1\n",
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            "3\n",
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            "1\n",
            "2\n",
            "49\n",
            "30\n",
            "1\n",
            "22\n",
            "2\n",
            "GOOD Q (left to the green): 0.9757227882773283 , BAD Q (under the red) -0.3373815303299611 , BAD Q 2(left of the red) 0.18329618368277922\n",
            "GOOD V (left to the green): 0.9757227882773283 , BAD V (under the red) 0.18329618368277922\n",
            "MSE_value= tensor(0.2087, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.10,0.65)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.08)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.40,4.09)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.85)\n",
            "\n",
            "────────────╭●─────────Rot(0.01,-1.38,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.10,1.94,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.14,6.29,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.99,-5.55,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2087, dtype=torch.float64)\n",
            "GAME = 360, LOSSavg20 = 0.12876664, number of steps done in last= 2, last_episode's_last_state = 7, epsilon = 0.7695, game_rewardavg20 = 0.0571\n",
            "19\n",
            "41\n",
            "4\n",
            "29\n",
            "1\n",
            "49\n",
            "3\n",
            "24\n",
            "3\n",
            "33\n",
            "16\n",
            "51\n",
            "14\n",
            "71\n",
            "8\n",
            "4\n",
            "3\n",
            "2\n",
            "2\n",
            "24\n",
            "GOOD Q (left to the green): 0.975078860077942 , BAD Q (under the red) -0.2893560844147285 , BAD Q 2(left of the red) 0.1640760196014337\n",
            "GOOD V (left to the green): 0.975078860077942 , BAD V (under the red) 0.1640760196014337\n",
            "MSE_value= tensor(0.2147, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.09,0.65)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.07)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.42,4.10)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.64,-1.83)\n",
            "\n",
            "────────────╭●─────────Rot(0.02,-1.36,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.10,1.94,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.03,6.29,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.97,-5.56,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2147, dtype=torch.float64)\n",
            "GAME = 380, LOSSavg20 = 0.12206493, number of steps done in last= 24, last_episode's_last_state = 11, epsilon = 0.7595, game_rewardavg20 = 0.0581\n",
            "60\n",
            "2\n",
            "9\n",
            "19\n",
            "3\n",
            "3\n",
            "13\n",
            "9\n",
            "2\n",
            "21\n",
            "16\n",
            "20\n",
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            "11\n",
            "2\n",
            "21\n",
            "5\n",
            "6\n",
            "7\n",
            "18\n",
            "GOOD Q (left to the green): 0.948989480391212 , BAD Q (under the red) -0.3965572928194776 , BAD Q 2(left of the red) 0.16090794777983553\n",
            "GOOD V (left to the green): 0.948989480391212 , BAD V (under the red) 0.16090794777983553\n",
            "MSE_value= tensor(0.2149, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.11,0.63)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.09)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.42,4.12)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.64,-1.82)\n",
            "\n",
            "────────────╭●─────────Rot(0.00,-1.38,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.12,1.95,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.00,6.29,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.96,-5.56,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2149, dtype=torch.float64)\n",
            "GAME = 400, LOSSavg20 = 0.11599035, number of steps done in last= 18, last_episode's_last_state = 7, epsilon = 0.7495, game_rewardavg20 = 0.0627\n",
            "32\n",
            "70\n",
            "2\n",
            "1\n",
            "4\n",
            "1\n",
            "14\n",
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            "10\n",
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            "27\n",
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            "20\n",
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            "7\n",
            "12\n",
            "2\n",
            "2\n",
            "11\n",
            "GOOD Q (left to the green): 0.9266824070745441 , BAD Q (under the red) -0.45453838510290495 , BAD Q 2(left of the red) 0.1529255436387863\n",
            "GOOD V (left to the green): 0.9266824070745441 , BAD V (under the red) 0.1529255436387863\n",
            "MSE_value= tensor(0.2150, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.12,0.62)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.94,-2.10)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.43,4.14)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.62,-1.83)\n",
            "\n",
            "────────────╭●─────────Rot(-0.01,-1.40,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.13,1.95,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.00,6.29,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.97,-5.54,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2150, dtype=torch.float64)\n",
            "GAME = 420, LOSSavg20 = 0.11207745, number of steps done in last= 11, last_episode's_last_state = 11, epsilon = 0.7395, game_rewardavg20 = 0.0612\n",
            "2\n",
            "6\n",
            "17\n",
            "8\n",
            "4\n",
            "2\n",
            "3\n",
            "2\n",
            "2\n",
            "3\n",
            "5\n",
            "2\n",
            "24\n",
            "7\n",
            "2\n",
            "2\n",
            "2\n",
            "13\n",
            "3\n",
            "4\n",
            "GOOD Q (left to the green): 0.9096837687064162 , BAD Q (under the red) -0.5029217939697311 , BAD Q 2(left of the red) 0.15528448387125604\n",
            "GOOD V (left to the green): 0.9096837687064162 , BAD V (under the red) 0.15528448387125604\n",
            "MSE_value= tensor(0.2134, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.61)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.95,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.43,4.15)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.62,-1.83)\n",
            "\n",
            "────────────╭●─────────Rot(-0.02,-1.41,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.13,1.96,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.00,6.28,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.97,-5.54,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2134, dtype=torch.float64)\n",
            "GAME = 440, LOSSavg20 = 0.10742392, number of steps done in last= 4, last_episode's_last_state = 11, epsilon = 0.7295, game_rewardavg20 = 0.0622\n",
            "16\n",
            "8\n",
            "2\n",
            "45\n",
            "84\n",
            "4\n",
            "168\n",
            "4\n",
            "1\n",
            "21\n",
            "8\n",
            "73\n",
            "5\n",
            "7\n",
            "7\n",
            "17\n",
            "3\n",
            "3\n",
            "17\n",
            "67\n",
            "GOOD Q (left to the green): 0.8797559896906315 , BAD Q (under the red) -0.5787477923998114 , BAD Q 2(left of the red) 0.13435004518675853\n",
            "GOOD V (left to the green): 0.8797559896906315 , BAD V (under the red) 0.13506597728002862\n",
            "MSE_value= tensor(0.2217, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.14,0.60)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.96,-2.12)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.43,4.18)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.81)\n",
            "\n",
            "────────────╭●─────────Rot(-0.05,-1.43,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.15,1.96,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.13,6.30,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.95,-5.55,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2217, dtype=torch.float64)\n",
            "GAME = 460, LOSSavg20 = 0.10335546, number of steps done in last= 67, last_episode's_last_state = 7, epsilon = 0.7195, game_rewardavg20 = 0.0793\n",
            "8\n",
            "1\n",
            "50\n",
            "5\n",
            "77\n",
            "10\n",
            "3\n",
            "2\n",
            "4\n",
            "11\n",
            "10\n",
            "8\n",
            "4\n",
            "24\n",
            "3\n",
            "50\n",
            "5\n",
            "42\n",
            "41\n",
            "3\n",
            "GOOD Q (left to the green): 0.8632665328647268 , BAD Q (under the red) -0.550976713717572 , BAD Q 2(left of the red) 0.13581766173148468\n",
            "GOOD V (left to the green): 0.8632665328647268 , BAD V (under the red) 0.13581766173148468\n",
            "MSE_value= tensor(0.2212, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.14,0.61)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.96,-2.10)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.43,4.18)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.80)\n",
            "\n",
            "────────────╭●─────────Rot(-0.05,-1.43,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.17,1.95,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.16,6.32,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.94,-5.55,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2212, dtype=torch.float64)\n",
            "GAME = 480, LOSSavg20 = 0.09943161, number of steps done in last= 3, last_episode's_last_state = 11, epsilon = 0.7095, game_rewardavg20 = 0.0614\n",
            "11\n",
            "4\n",
            "41\n",
            "2\n",
            "1\n",
            "2\n",
            "6\n",
            "16\n",
            "34\n",
            "128\n",
            "3\n",
            "2\n",
            "34\n",
            "52\n",
            "20\n",
            "36\n",
            "4\n",
            "5\n",
            "20\n",
            "36\n",
            "GOOD Q (left to the green): 0.8563349957836489 , BAD Q (under the red) -0.5443076004366754 , BAD Q 2(left of the red) 0.13500392793482519\n",
            "GOOD V (left to the green): 0.8563349957836489 , BAD V (under the red) 0.13500392793482519\n",
            "MSE_value= tensor(0.2216, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.61)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.96,-2.09)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.18)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.62,-1.80)\n",
            "\n",
            "────────────╭●─────────Rot(-0.06,-1.44,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.20,1.94,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.21,6.33,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.93,-5.54,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2216, dtype=torch.float64)\n",
            "GAME = 500, LOSSavg20 = 0.09704490, number of steps done in last= 36, last_episode's_last_state = 7, epsilon = 0.6995, game_rewardavg20 = 0.0605\n",
            "13\n",
            "19\n",
            "26\n",
            "9\n",
            "4\n",
            "3\n",
            "2\n",
            "2\n",
            "3\n",
            "15\n",
            "14\n",
            "50\n",
            "1\n",
            "5\n",
            "2\n",
            "2\n",
            "6\n",
            "1\n",
            "3\n",
            "36\n",
            "GOOD Q (left to the green): 0.8626329802192688 , BAD Q (under the red) -0.5385468259515032 , BAD Q 2(left of the red) 0.1293489204623658\n",
            "GOOD V (left to the green): 0.8626329802192688 , BAD V (under the red) 0.1293489204623658\n",
            "MSE_value= tensor(0.2244, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.61)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.96,-2.09)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.18)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.60,-1.81)\n",
            "\n",
            "────────────╭●─────────Rot(-0.06,-1.44,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.21,1.94,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.23,6.33,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.94,-5.52,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2244, dtype=torch.float64)\n",
            "GAME = 520, LOSSavg20 = 0.09456393, number of steps done in last= 36, last_episode's_last_state = 11, epsilon = 0.6895, game_rewardavg20 = 0.0572\n",
            "2\n",
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            "20\n",
            "9\n",
            "GOOD Q (left to the green): 0.8809900037520259 , BAD Q (under the red) -0.4170483176906807 , BAD Q 2(left of the red) 0.12694912761334076\n",
            "GOOD V (left to the green): 0.8809900037520259 , BAD V (under the red) 0.12694912761334076\n",
            "MSE_value= tensor(0.2246, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.10,0.63)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.94,-2.06)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.46,4.17)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.59,-1.82)\n",
            "\n",
            "────────────╭●─────────Rot(-0.03,-1.41,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.23,1.93,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.26,6.31,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.95,-5.51,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2246, dtype=torch.float64)\n",
            "GAME = 540, LOSSavg20 = 0.09228811, number of steps done in last= 9, last_episode's_last_state = 7, epsilon = 0.6795, game_rewardavg20 = 0.0688\n",
            "23\n",
            "52\n",
            "18\n",
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            "3\n",
            "91\n",
            "3\n",
            "4\n",
            "20\n",
            "21\n",
            "26\n",
            "85\n",
            "GOOD Q (left to the green): 0.8151224111030169 , BAD Q (under the red) -0.6065798902735647 , BAD Q 2(left of the red) 0.12233271989600764\n",
            "GOOD V (left to the green): 0.822561174657561 , BAD V (under the red) 0.12233271989600764\n",
            "MSE_value= tensor(0.2277, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.14,0.59)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.98,-2.09)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.45,4.20)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.59,-1.81)\n",
            "\n",
            "────────────╭●─────────Rot(-0.08,-1.46,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.28,1.94,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.32,6.29,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.94,-5.51,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2277, dtype=torch.float64)\n",
            "GAME = 560, LOSSavg20 = 0.08960170, number of steps done in last= 85, last_episode's_last_state = 7, epsilon = 0.6695, game_rewardavg20 = 0.0657\n",
            "40\n",
            "13\n",
            "64\n",
            "6\n",
            "3\n",
            "2\n",
            "3\n",
            "74\n",
            "2\n",
            "1\n",
            "1\n",
            "3\n",
            "9\n",
            "3\n",
            "191\n",
            "1\n",
            "4\n",
            "8\n",
            "9\n",
            "2\n",
            "GOOD Q (left to the green): 0.8748794866239887 , BAD Q (under the red) -0.38149310805669406 , BAD Q 2(left of the red) 0.09458308846374952\n",
            "GOOD V (left to the green): 0.8748794866239887 , BAD V (under the red) 0.10581794842691888\n",
            "MSE_value= tensor(0.2328, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.09,0.64)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.05)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.48,4.18)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.59,-1.80)\n",
            "\n",
            "────────────╭●─────────Rot(-0.03,-1.41,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.27,1.93,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.22,6.28,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.93,-5.51,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2328, dtype=torch.float64)\n",
            "GAME = 580, LOSSavg20 = 0.08718663, number of steps done in last= 2, last_episode's_last_state = 11, epsilon = 0.6595, game_rewardavg20 = 0.0587\n",
            "55\n",
            "6\n",
            "40\n",
            "1\n",
            "73\n",
            "1\n",
            "95\n",
            "47\n",
            "1\n",
            "3\n",
            "6\n",
            "16\n",
            "13\n",
            "12\n",
            "3\n",
            "6\n",
            "6\n",
            "2\n",
            "6\n",
            "11\n",
            "GOOD Q (left to the green): 0.912497588538121 , BAD Q (under the red) -0.25263875056531904 , BAD Q 2(left of the red) 0.10866996040510146\n",
            "GOOD V (left to the green): 0.912497588538121 , BAD V (under the red) 0.10866996040510146\n",
            "MSE_value= tensor(0.2334, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.06,0.66)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.49,4.16)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.76)\n",
            "\n",
            "────────────╭●─────────Rot(-0.00,-1.38,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.29,1.92,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.20,6.29,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.89,-5.55,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2334, dtype=torch.float64)\n",
            "GAME = 600, LOSSavg20 = 0.08537708, number of steps done in last= 11, last_episode's_last_state = 7, epsilon = 0.6495, game_rewardavg20 = 0.0653\n",
            "3\n",
            "23\n",
            "16\n",
            "1\n",
            "18\n",
            "27\n",
            "10\n",
            "8\n",
            "20\n",
            "9\n",
            "17\n",
            "2\n",
            "7\n",
            "1\n",
            "4\n",
            "9\n",
            "17\n",
            "3\n",
            "6\n",
            "3\n",
            "GOOD Q (left to the green): 0.9203607708692734 , BAD Q (under the red) -0.2438701281572962 , BAD Q 2(left of the red) 0.12405717630158014\n",
            "GOOD V (left to the green): 0.9203607708692734 , BAD V (under the red) 0.12405717630158014\n",
            "MSE_value= tensor(0.2269, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.06,0.66)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.49,4.15)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.76)\n",
            "\n",
            "────────────╭●─────────Rot(0.00,-1.38,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.30,1.91,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.15,6.29,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.55,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2269, dtype=torch.float64)\n",
            "GAME = 620, LOSSavg20 = 0.08336877, number of steps done in last= 3, last_episode's_last_state = 7, epsilon = 0.6395, game_rewardavg20 = 0.0677\n",
            "44\n",
            "6\n",
            "5\n",
            "4\n",
            "11\n",
            "5\n",
            "31\n",
            "12\n",
            "3\n",
            "14\n",
            "16\n",
            "3\n",
            "2\n",
            "1\n",
            "19\n",
            "46\n",
            "8\n",
            "8\n",
            "10\n",
            "9\n",
            "GOOD Q (left to the green): 0.936612702780767 , BAD Q (under the red) -0.2429918642802688 , BAD Q 2(left of the red) 0.11647486138076923\n",
            "GOOD V (left to the green): 0.936612702780767 , BAD V (under the red) 0.11647486138076923\n",
            "MSE_value= tensor(0.2307, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.05,0.66)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.49,4.14)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.77)\n",
            "\n",
            "────────────╭●─────────Rot(0.00,-1.38,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.31,1.92,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.11,6.28,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.55,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2307, dtype=torch.float64)\n",
            "GAME = 640, LOSSavg20 = 0.08142087, number of steps done in last= 9, last_episode's_last_state = 7, epsilon = 0.6295, game_rewardavg20 = 0.0653\n",
            "21\n",
            "3\n",
            "17\n",
            "24\n",
            "5\n",
            "14\n",
            "21\n",
            "13\n",
            "7\n",
            "33\n",
            "51\n",
            "14\n",
            "29\n",
            "20\n",
            "16\n",
            "1\n",
            "3\n",
            "15\n",
            "11\n",
            "7\n",
            "GOOD Q (left to the green): 0.8920597860030726 , BAD Q (under the red) -0.393680312257087 , BAD Q 2(left of the red) 0.11010095811802578\n",
            "GOOD V (left to the green): 0.8920597860030726 , BAD V (under the red) 0.11010095811802578\n",
            "MSE_value= tensor(0.2317, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.08,0.62)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.95,-2.05)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.49,4.17)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.77)\n",
            "\n",
            "────────────╭●─────────Rot(-0.03,-1.41,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.34,1.93,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.16,6.27,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.89,-5.54,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2317, dtype=torch.float64)\n",
            "GAME = 660, LOSSavg20 = 0.07966681, number of steps done in last= 7, last_episode's_last_state = 7, epsilon = 0.6195, game_rewardavg20 = 0.0607\n",
            "3\n",
            "29\n",
            "6\n",
            "16\n",
            "17\n",
            "11\n",
            "20\n",
            "3\n",
            "11\n",
            "9\n",
            "4\n",
            "4\n",
            "13\n",
            "5\n",
            "3\n",
            "12\n",
            "3\n",
            "12\n",
            "6\n",
            "5\n",
            "GOOD Q (left to the green): 0.8625561233193898 , BAD Q (under the red) -0.4580287953398969 , BAD Q 2(left of the red) 0.10504422416696557\n",
            "GOOD V (left to the green): 0.8625561233193898 , BAD V (under the red) 0.10504422416696557\n",
            "MSE_value= tensor(0.2352, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.10,0.61)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.96,-2.06)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.48,4.18)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.76)\n",
            "\n",
            "────────────╭●─────────Rot(-0.04,-1.42,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.35,1.93,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.17,6.27,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.89,-5.54,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2352, dtype=torch.float64)\n",
            "GAME = 680, LOSSavg20 = 0.07811656, number of steps done in last= 5, last_episode's_last_state = 7, epsilon = 0.6095, game_rewardavg20 = 0.0522\n",
            "7\n",
            "1\n",
            "12\n",
            "2\n",
            "2\n",
            "2\n",
            "7\n",
            "3\n",
            "2\n",
            "4\n",
            "7\n",
            "12\n",
            "14\n",
            "14\n",
            "35\n",
            "9\n",
            "22\n",
            "24\n",
            "17\n",
            "20\n",
            "GOOD Q (left to the green): 0.8481602804167764 , BAD Q (under the red) -0.5644824066919517 , BAD Q 2(left of the red) 0.09802920367191825\n",
            "GOOD V (left to the green): 0.8481602804167764 , BAD V (under the red) 0.09802920367191825\n",
            "MSE_value= tensor(0.2382, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.11,0.59)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.98,-2.08)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.48,4.19)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.76)\n",
            "\n",
            "────────────╭●─────────Rot(-0.07,-1.44,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.37,1.95,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.18,6.28,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.89,-5.55,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2382, dtype=torch.float64)\n",
            "GAME = 700, LOSSavg20 = 0.07622661, number of steps done in last= 20, last_episode's_last_state = 11, epsilon = 0.5995, game_rewardavg20 = 0.0404\n",
            "1\n",
            "6\n",
            "2\n",
            "9\n",
            "27\n",
            "6\n",
            "23\n",
            "65\n",
            "22\n",
            "15\n",
            "2\n",
            "4\n",
            "4\n",
            "11\n",
            "35\n",
            "17\n",
            "14\n",
            "8\n",
            "8\n",
            "5\n",
            "GOOD Q (left to the green): 0.8323431529237629 , BAD Q (under the red) -0.7003615106385657 , BAD Q 2(left of the red) 0.09400853582354163\n",
            "GOOD V (left to the green): 0.8323431529237629 , BAD V (under the red) 0.09400853582354163\n",
            "MSE_value= tensor(0.2395, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.57)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-7.00,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.48,4.19)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.61,-1.77)\n",
            "\n",
            "────────────╭●─────────Rot(-0.11,-1.48,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.39,1.96,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.20,6.30,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.90,-5.53,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2395, dtype=torch.float64)\n",
            "GAME = 720, LOSSavg20 = 0.07516637, number of steps done in last= 5, last_episode's_last_state = 7, epsilon = 0.5895, game_rewardavg20 = 0.0388\n",
            "4\n",
            "4\n",
            "4\n",
            "1\n",
            "15\n",
            "20\n",
            "1\n",
            "24\n",
            "1\n",
            "7\n",
            "27\n",
            "1\n",
            "3\n",
            "1\n",
            "1\n",
            "6\n",
            "10\n",
            "5\n",
            "5\n",
            "5\n",
            "GOOD Q (left to the green): 0.8449921938528697 , BAD Q (under the red) -0.7007040448281459 , BAD Q 2(left of the red) 0.09818762869147657\n",
            "GOOD V (left to the green): 0.8449921938528697 , BAD V (under the red) 0.09818762869147657\n",
            "MSE_value= tensor(0.2326, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.12,0.57)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-7.01,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.49,4.18)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.58,-1.80)\n",
            "\n",
            "────────────╭●─────────Rot(-0.11,-1.48,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.40,1.96,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.18,6.30,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.93,-5.50,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2326, dtype=torch.float64)\n",
            "GAME = 740, LOSSavg20 = 0.07396012, number of steps done in last= 5, last_episode's_last_state = 11, epsilon = 0.5795, game_rewardavg20 = 0.0336\n",
            "20\n",
            "69\n",
            "5\n",
            "3\n",
            "8\n",
            "139\n",
            "5\n",
            "2\n",
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            "116\n",
            "1\n",
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            "1\n",
            "2\n",
            "54\n",
            "27\n",
            "22\n",
            "128\n",
            "3\n",
            "GOOD Q (left to the green): 0.9342833102082686 , BAD Q (under the red) -0.4378487416545164 , BAD Q 2(left of the red) 0.05682339188111235\n",
            "GOOD V (left to the green): 0.9342833102082686 , BAD V (under the red) 0.09083451249248686\n",
            "MSE_value= tensor(0.2352, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.06,0.62)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.95,-2.06)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.48,4.17)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.59,-1.80)\n",
            "\n",
            "────────────╭●─────────Rot(-0.06,-1.43,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.37,1.97,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.27,6.35,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.93,-5.51,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2352, dtype=torch.float64)\n",
            "GAME = 760, LOSSavg20 = 0.07293401, number of steps done in last= 3, last_episode's_last_state = 11, epsilon = 0.5695, game_rewardavg20 = 0.0334\n",
            "117\n",
            "1\n",
            "3\n",
            "2\n",
            "5\n",
            "2\n",
            "4\n",
            "2\n",
            "18\n",
            "33\n",
            "91\n",
            "47\n",
            "2\n",
            "9\n",
            "2\n",
            "42\n",
            "19\n",
            "4\n",
            "7\n",
            "12\n",
            "GOOD Q (left to the green): 0.9732521802642768 , BAD Q (under the red) -0.2118707451419729 , BAD Q 2(left of the red) 0.10099226533138278\n",
            "GOOD V (left to the green): 0.9732521802642768 , BAD V (under the red) 0.10099226533138278\n",
            "MSE_value= tensor(0.2363, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.02,0.66)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.49,4.14)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.64,-1.76)\n",
            "\n",
            "────────────╭●─────────Rot(-0.00,-1.38,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●──────────────────────Rot(-4.39,1.93,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X─────────────────────╭●──────────────────────Rot(0.33,6.33,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)────────────────────────╰X──────────────────────Rot(1.88,-5.56,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2363, dtype=torch.float64)\n",
            "GAME = 780, LOSSavg20 = 0.07267031, number of steps done in last= 12, last_episode's_last_state = 11, epsilon = 0.5595, game_rewardavg20 = 0.0418\n",
            "2\n",
            "20\n",
            "29\n",
            "4\n",
            "6\n",
            "7\n",
            "75\n",
            "2\n",
            "22\n",
            "2\n",
            "1\n",
            "11\n",
            "3\n",
            "1\n",
            "21\n",
            "53\n",
            "10\n",
            "25\n",
            "8\n",
            "6\n",
            "GOOD Q (left to the green): 0.9537486754996767 , BAD Q (under the red) -0.16790231393435895 , BAD Q 2(left of the red) 0.08420926503354975\n",
            "GOOD V (left to the green): 0.9537486754996767 , BAD V (under the red) 0.08420926503354975\n",
            "MSE_value= tensor(0.2432, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.02,0.67)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.50,4.15)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.77)\n",
            "\n",
            "────────────╭●─────────Rot(0.02,-1.36,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.39,1.93,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.33,6.34,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.90,-5.55,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2432, dtype=torch.float64)\n",
            "GAME = 800, LOSSavg20 = 0.07194255, number of steps done in last= 6, last_episode's_last_state = 11, epsilon = 0.5495, game_rewardavg20 = 0.0440\n",
            "3\n",
            "4\n",
            "21\n",
            "10\n",
            "29\n",
            "20\n",
            "46\n",
            "2\n",
            "1\n",
            "16\n",
            "1\n",
            "48\n",
            "13\n",
            "4\n",
            "17\n",
            "20\n",
            "43\n",
            "85\n",
            "35\n",
            "3\n",
            "GOOD Q (left to the green): 0.9469298763808306 , BAD Q (under the red) -0.1328151322530926 , BAD Q 2(left of the red) 0.05787922257445921\n",
            "GOOD V (left to the green): 0.9469298763808306 , BAD V (under the red) 0.05787922257445921\n",
            "MSE_value= tensor(0.2501, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.02,0.67)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.52,4.17)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.77)\n",
            "\n",
            "────────────╭●─────────Rot(0.03,-1.34,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.38,1.93,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.28,6.33,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.90,-5.55,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2501, dtype=torch.float64)\n",
            "GAME = 820, LOSSavg20 = 0.07077873, number of steps done in last= 3, last_episode's_last_state = 7, epsilon = 0.5395, game_rewardavg20 = 0.0453\n",
            "51\n",
            "6\n",
            "47\n",
            "218\n",
            "4\n",
            "10\n",
            "4\n",
            "6\n",
            "1\n",
            "11\n",
            "17\n",
            "20\n",
            "18\n",
            "18\n",
            "2\n",
            "1\n",
            "2\n",
            "1\n",
            "4\n",
            "5\n",
            "GOOD Q (left to the green): 0.9575221002424645 , BAD Q (under the red) 0.007410944369834249 , BAD Q 2(left of the red) 0.06755251364231435\n",
            "GOOD V (left to the green): 0.9575221002424645 , BAD V (under the red) 0.06755251364231435\n",
            "MSE_value= tensor(0.2412, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.00,0.70)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-1.99)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.52,4.16)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.68,-1.73)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.31,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.91,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.38,6.28,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.86,-5.60,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2412, dtype=torch.float64)\n",
            "GAME = 840, LOSSavg20 = 0.06983973, number of steps done in last= 5, last_episode's_last_state = 7, epsilon = 0.5295, game_rewardavg20 = 0.0392\n",
            "52\n",
            "4\n",
            "2\n",
            "3\n",
            "3\n",
            "6\n",
            "5\n",
            "7\n",
            "13\n",
            "3\n",
            "15\n",
            "11\n",
            "11\n",
            "23\n",
            "18\n",
            "7\n",
            "6\n",
            "5\n",
            "12\n",
            "20\n",
            "GOOD Q (left to the green): 0.9282174712517061 , BAD Q (under the red) -0.06189614741806637 , BAD Q 2(left of the red) 0.09450481897898494\n",
            "GOOD V (left to the green): 0.9282174712517061 , BAD V (under the red) 0.09450481897898494\n",
            "MSE_value= tensor(0.2384, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.01,0.68)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.52,4.16)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.68,-1.73)\n",
            "\n",
            "────────────╭●─────────Rot(0.05,-1.32,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.43,1.90,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.29,6.33,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.86,-5.59,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2384, dtype=torch.float64)\n",
            "GAME = 860, LOSSavg20 = 0.06835990, number of steps done in last= 20, last_episode's_last_state = 7, epsilon = 0.5195, game_rewardavg20 = 0.0429\n",
            "3\n",
            "5\n",
            "4\n",
            "4\n",
            "10\n",
            "3\n",
            "4\n",
            "5\n",
            "4\n",
            "9\n",
            "3\n",
            "4\n",
            "1\n",
            "21\n",
            "2\n",
            "1\n",
            "1\n",
            "13\n",
            "5\n",
            "14\n",
            "GOOD Q (left to the green): 0.925057428296502 , BAD Q (under the red) -0.12637408333831168 , BAD Q 2(left of the red) 0.09116869927266291\n",
            "GOOD V (left to the green): 0.925057428296502 , BAD V (under the red) 0.09116869927266291\n",
            "MSE_value= tensor(0.2411, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.02,0.67)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.51,4.16)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.67,-1.73)\n",
            "\n",
            "────────────╭●─────────Rot(0.04,-1.34,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.43,1.91,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.23,6.34,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.86,-5.59,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2411, dtype=torch.float64)\n",
            "GAME = 880, LOSSavg20 = 0.06773024, number of steps done in last= 14, last_episode's_last_state = 7, epsilon = 0.5095, game_rewardavg20 = 0.0391\n",
            "9\n",
            "2\n",
            "15\n",
            "3\n",
            "4\n",
            "17\n",
            "4\n",
            "5\n",
            "3\n",
            "4\n",
            "28\n",
            "4\n",
            "1\n",
            "11\n",
            "2\n",
            "6\n",
            "21\n",
            "1\n",
            "19\n",
            "21\n",
            "GOOD Q (left to the green): 0.931915084123361 , BAD Q (under the red) -0.17957473947452968 , BAD Q 2(left of the red) 0.08370628781424888\n",
            "GOOD V (left to the green): 0.931915084123361 , BAD V (under the red) 0.08370628781424888\n",
            "MSE_value= tensor(0.2443, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.66)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.94,-2.03)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.51,4.16)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.67,-1.74)\n",
            "\n",
            "────────────╭●─────────Rot(0.03,-1.35,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.42,1.92,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.19,6.34,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.87,-5.58,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2443, dtype=torch.float64)\n",
            "GAME = 900, LOSSavg20 = 0.06712514, number of steps done in last= 21, last_episode's_last_state = 7, epsilon = 0.4995, game_rewardavg20 = 0.0364\n",
            "11\n",
            "14\n",
            "7\n",
            "14\n",
            "3\n",
            "9\n",
            "3\n",
            "86\n",
            "7\n",
            "3\n",
            "3\n",
            "43\n",
            "2\n",
            "20\n",
            "11\n",
            "11\n",
            "1\n",
            "133\n",
            "37\n",
            "32\n",
            "GOOD Q (left to the green): 0.935968324891556 , BAD Q (under the red) -0.24475950758565396 , BAD Q 2(left of the red) 0.016969810497330706\n",
            "GOOD V (left to the green): 0.935968324891556 , BAD V (under the red) 0.03708005428672606\n",
            "MSE_value= tensor(0.2506, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.04,0.64)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.05)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.53,4.19)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.78)\n",
            "\n",
            "────────────╭●─────────Rot(0.01,-1.36,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.37,1.96,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.16,6.35,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.91,-5.54,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2506, dtype=torch.float64)\n",
            "GAME = 920, LOSSavg20 = 0.06646530, number of steps done in last= 32, last_episode's_last_state = 11, epsilon = 0.4895, game_rewardavg20 = 0.0365\n",
            "28\n",
            "15\n",
            "6\n",
            "121\n",
            "157\n",
            "5\n",
            "2\n",
            "111\n",
            "8\n",
            "16\n",
            "57\n",
            "17\n",
            "61\n",
            "3\n",
            "43\n",
            "9\n",
            "6\n",
            "5\n",
            "31\n",
            "33\n",
            "GOOD Q (left to the green): 0.9288119682365834 , BAD Q (under the red) -0.0036725162692115565 , BAD Q 2(left of the red) 0.07612445516915933\n",
            "GOOD V (left to the green): 0.9288119682365834 , BAD V (under the red) 0.07612445516915933\n",
            "MSE_value= tensor(0.2350, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.01,0.70)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.53,4.17)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.64,-1.77)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.31,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.90,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.24,6.34,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.56,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2350, dtype=torch.float64)\n",
            "GAME = 940, LOSSavg20 = 0.06666061, number of steps done in last= 33, last_episode's_last_state = 7, epsilon = 0.4795, game_rewardavg20 = 0.0354\n",
            "3\n",
            "5\n",
            "6\n",
            "26\n",
            "19\n",
            "6\n",
            "10\n",
            "5\n",
            "1\n",
            "2\n",
            "1\n",
            "8\n",
            "2\n",
            "2\n",
            "42\n",
            "3\n",
            "3\n",
            "31\n",
            "1\n",
            "3\n",
            "GOOD Q (left to the green): 0.945461727883296 , BAD Q (under the red) 0.01696126204835835 , BAD Q 2(left of the red) 0.059918950701630835\n",
            "GOOD V (left to the green): 0.945461727883296 , BAD V (under the red) 0.059918950701630835\n",
            "MSE_value= tensor(0.2399, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.00,0.70)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.53,4.17)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.64,-1.76)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.30,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.91,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.19,6.33,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.56,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2399, dtype=torch.float64)\n",
            "GAME = 960, LOSSavg20 = 0.06595829, number of steps done in last= 3, last_episode's_last_state = 7, epsilon = 0.4695, game_rewardavg20 = 0.0385\n",
            "26\n",
            "3\n",
            "15\n",
            "10\n",
            "3\n",
            "6\n",
            "21\n",
            "1\n",
            "19\n",
            "19\n",
            "1\n",
            "8\n",
            "26\n",
            "10\n",
            "2\n",
            "1\n",
            "4\n",
            "3\n",
            "9\n",
            "6\n",
            "GOOD Q (left to the green): 0.9391732829482122 , BAD Q (under the red) -0.036127067638168864 , BAD Q 2(left of the red) 0.06497690677692153\n",
            "GOOD V (left to the green): 0.9391732829482122 , BAD V (under the red) 0.06497690677692153\n",
            "MSE_value= tensor(0.2444, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.01,0.69)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.54,4.17)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.63,-1.77)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.31,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.91,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.15,6.34,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.90,-5.55,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2444, dtype=torch.float64)\n",
            "GAME = 980, LOSSavg20 = 0.06489953, number of steps done in last= 6, last_episode's_last_state = 11, epsilon = 0.4595, game_rewardavg20 = 0.0407\n",
            "6\n",
            "6\n",
            "67\n",
            "89\n",
            "2\n",
            "8\n",
            "4\n",
            "11\n",
            "9\n",
            "1\n",
            "2\n",
            "12\n",
            "25\n",
            "32\n",
            "1\n",
            "25\n",
            "10\n",
            "2\n",
            "23\n",
            "4\n",
            "GOOD Q (left to the green): 0.922178831150134 , BAD Q (under the red) -0.13219627933793698 , BAD Q 2(left of the red) 0.0687521678974643\n",
            "GOOD V (left to the green): 0.922178831150134 , BAD V (under the red) 0.0687521678974643\n",
            "MSE_value= tensor(0.2479, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.67)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.52,4.17)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.64,-1.76)\n",
            "\n",
            "────────────╭●─────────Rot(0.03,-1.34,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.42,1.92,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.16,6.34,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.56,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2479, dtype=torch.float64)\n",
            "GAME = 1000, LOSSavg20 = 0.06432757, number of steps done in last= 4, last_episode's_last_state = 11, epsilon = 0.4495, game_rewardavg20 = 0.0489\n",
            "2\n",
            "37\n",
            "4\n",
            "4\n",
            "14\n",
            "5\n",
            "1\n",
            "1\n",
            "47\n",
            "23\n",
            "19\n",
            "6\n",
            "7\n",
            "12\n",
            "5\n",
            "16\n",
            "7\n",
            "11\n",
            "4\n",
            "5\n",
            "GOOD Q (left to the green): 0.9186173175232739 , BAD Q (under the red) -0.20230203755080645 , BAD Q 2(left of the red) 0.06617919952312262\n",
            "GOOD V (left to the green): 0.9186173175232739 , BAD V (under the red) 0.06617919952312262\n",
            "MSE_value= tensor(0.2492, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.04,0.66)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.94,-2.04)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.51,4.17)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.65,-1.76)\n",
            "\n",
            "────────────╭●─────────Rot(0.02,-1.35,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.42,1.93,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.17,6.35,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.57,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2492, dtype=torch.float64)\n",
            "GAME = 1020, LOSSavg20 = 0.06366052, number of steps done in last= 5, last_episode's_last_state = 11, epsilon = 0.4395, game_rewardavg20 = 0.0475\n",
            "8\n",
            "1\n",
            "56\n",
            "1\n",
            "5\n",
            "5\n",
            "98\n",
            "6\n",
            "24\n",
            "11\n",
            "37\n",
            "13\n",
            "31\n",
            "1\n",
            "104\n",
            "35\n",
            "3\n",
            "16\n",
            "1\n",
            "6\n",
            "GOOD Q (left to the green): 0.9233795977240236 , BAD Q (under the red) -0.22700093633928525 , BAD Q 2(left of the red) 0.02609380505603731\n",
            "GOOD V (left to the green): 0.9233795977240236 , BAD V (under the red) 0.05119665870260559\n",
            "MSE_value= tensor(0.2533, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.04,0.66)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.05)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.52,4.18)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.66,-1.76)\n",
            "\n",
            "────────────╭●─────────Rot(0.01,-1.36,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.95,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.18,6.35,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.58,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2533, dtype=torch.float64)\n",
            "GAME = 1040, LOSSavg20 = 0.06296515, number of steps done in last= 6, last_episode's_last_state = 11, epsilon = 0.4295, game_rewardavg20 = 0.0431\n",
            "60\n",
            "11\n",
            "52\n",
            "24\n",
            "4\n",
            "1\n",
            "40\n",
            "10\n",
            "4\n",
            "2\n",
            "3\n",
            "13\n",
            "42\n",
            "4\n",
            "52\n",
            "1\n",
            "21\n",
            "13\n",
            "2\n",
            "1\n",
            "GOOD Q (left to the green): 0.9127415367233322 , BAD Q (under the red) -0.17435364986306656 , BAD Q 2(left of the red) 0.07277792271051892\n",
            "GOOD V (left to the green): 0.9127415367233322 , BAD V (under the red) 0.07277792271051892\n",
            "MSE_value= tensor(0.2476, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.67)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.03)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.53,4.16)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.65,-1.76)\n",
            "\n",
            "────────────╭●─────────Rot(0.03,-1.35,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.45,1.92,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.20,6.35,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.57,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2476, dtype=torch.float64)\n",
            "GAME = 1060, LOSSavg20 = 0.06245283, number of steps done in last= 1, last_episode's_last_state = 11, epsilon = 0.4195, game_rewardavg20 = 0.0392\n",
            "12\n",
            "16\n",
            "5\n",
            "6\n",
            "22\n",
            "5\n",
            "62\n",
            "23\n",
            "7\n",
            "29\n",
            "16\n",
            "1\n",
            "11\n",
            "7\n",
            "83\n",
            "5\n",
            "21\n",
            "2\n",
            "15\n",
            "3\n",
            "GOOD Q (left to the green): 0.8980928661349783 , BAD Q (under the red) -0.13634584475804395 , BAD Q 2(left of the red) 0.07845442906690858\n",
            "GOOD V (left to the green): 0.8980928661349783 , BAD V (under the red) 0.07845442906690858\n",
            "MSE_value= tensor(0.2450, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.68)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.51,4.16)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.64,-1.78)\n",
            "\n",
            "────────────╭●─────────Rot(0.03,-1.34,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.47,1.91,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.24,6.38,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.56,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2450, dtype=torch.float64)\n",
            "GAME = 1080, LOSSavg20 = 0.06160420, number of steps done in last= 3, last_episode's_last_state = 11, epsilon = 0.4095, game_rewardavg20 = 0.0396\n",
            "20\n",
            "8\n",
            "3\n",
            "20\n",
            "12\n",
            "1\n",
            "3\n",
            "3\n",
            "12\n",
            "34\n",
            "2\n",
            "3\n",
            "14\n",
            "47\n",
            "1\n",
            "2\n",
            "31\n",
            "11\n",
            "15\n",
            "1\n",
            "GOOD Q (left to the green): 0.8932773761660713 , BAD Q (under the red) -0.12723177429969285 , BAD Q 2(left of the red) 0.09038904128524072\n",
            "GOOD V (left to the green): 0.8932773761660713 , BAD V (under the red) 0.09038904128524072\n",
            "MSE_value= tensor(0.2365, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.68)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.50,4.15)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.64,-1.77)\n",
            "\n",
            "────────────╭●─────────Rot(0.03,-1.34,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.48,1.90,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.26,6.39,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.57,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2365, dtype=torch.float64)\n",
            "GAME = 1100, LOSSavg20 = 0.06131064, number of steps done in last= 1, last_episode's_last_state = 7, epsilon = 0.3995, game_rewardavg20 = 0.0399\n",
            "15\n",
            "35\n",
            "5\n",
            "20\n",
            "4\n",
            "23\n",
            "17\n",
            "1\n",
            "9\n",
            "9\n",
            "22\n",
            "4\n",
            "16\n",
            "23\n",
            "33\n",
            "34\n",
            "1\n",
            "1\n",
            "15\n",
            "21\n",
            "GOOD Q (left to the green): 0.902401689438854 , BAD Q (under the red) -0.12347456914386379 , BAD Q 2(left of the red) 0.08105859233187707\n",
            "GOOD V (left to the green): 0.902401689438854 , BAD V (under the red) 0.08105859233187707\n",
            "MSE_value= tensor(0.2418, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.68)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.01)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.52,4.15)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.62,-1.79)\n",
            "\n",
            "────────────╭●─────────Rot(0.03,-1.34,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.49,1.91,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.27,6.40,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.91,-5.55,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2418, dtype=torch.float64)\n",
            "GAME = 1120, LOSSavg20 = 0.06149579, number of steps done in last= 21, last_episode's_last_state = 11, epsilon = 0.3895, game_rewardavg20 = 0.0363\n",
            "6\n",
            "6\n",
            "4\n",
            "1\n",
            "4\n",
            "10\n",
            "2\n",
            "2\n",
            "1\n",
            "11\n",
            "27\n",
            "1\n",
            "8\n",
            "102\n",
            "3\n",
            "51\n",
            "14\n",
            "61\n",
            "75\n",
            "10\n",
            "GOOD Q (left to the green): 0.891701039225564 , BAD Q (under the red) -0.17746712835500894 , BAD Q 2(left of the red) 0.053528264431568995\n",
            "GOOD V (left to the green): 0.891701039225564 , BAD V (under the red) 0.053528264431568995\n",
            "MSE_value= tensor(0.2439, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.04,0.67)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.03)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.51,4.16)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.61,-1.80)\n",
            "\n",
            "────────────╭●─────────Rot(0.02,-1.35,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.48,1.92,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.28,6.40,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.91,-5.54,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2439, dtype=torch.float64)\n",
            "GAME = 1140, LOSSavg20 = 0.06164821, number of steps done in last= 10, last_episode's_last_state = 7, epsilon = 0.3795, game_rewardavg20 = 0.0359\n",
            "27\n",
            "19\n",
            "18\n",
            "2\n",
            "23\n",
            "8\n",
            "2\n",
            "11\n",
            "1\n",
            "49\n",
            "5\n",
            "11\n",
            "13\n",
            "1\n",
            "6\n",
            "18\n",
            "1\n",
            "83\n",
            "19\n",
            "15\n",
            "GOOD Q (left to the green): 0.9063194823074567 , BAD Q (under the red) -0.10360575370612946 , BAD Q 2(left of the red) 0.05948385049466326\n",
            "GOOD V (left to the green): 0.9063194823074567 , BAD V (under the red) 0.05948385049466326\n",
            "MSE_value= tensor(0.2430, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.69)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.01)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.51,4.16)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.61,-1.80)\n",
            "\n",
            "────────────╭●─────────Rot(0.03,-1.34,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.47,1.91,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.31,6.38,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.91,-5.54,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2430, dtype=torch.float64)\n",
            "GAME = 1160, LOSSavg20 = 0.06162633, number of steps done in last= 15, last_episode's_last_state = 11, epsilon = 0.3695, game_rewardavg20 = 0.0333\n",
            "38\n",
            "5\n",
            "19\n",
            "33\n",
            "1\n",
            "15\n",
            "1\n",
            "2\n",
            "10\n",
            "3\n",
            "26\n",
            "3\n",
            "71\n",
            "16\n",
            "29\n",
            "24\n",
            "4\n",
            "4\n",
            "66\n",
            "22\n",
            "GOOD Q (left to the green): 0.917526215096593 , BAD Q (under the red) 0.02322981053327483 , BAD Q 2(left of the red) 0.04966727723032627\n",
            "GOOD V (left to the green): 0.917526215096593 , BAD V (under the red) 0.04966727723032627\n",
            "MSE_value= tensor(0.2210, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.01,0.72)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.99)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.52,4.16)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.60,-1.82)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.31,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.48,1.90,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.35,6.40,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.92,-5.53,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2210, dtype=torch.float64)\n",
            "GAME = 1180, LOSSavg20 = 0.06197515, number of steps done in last= 22, last_episode's_last_state = 7, epsilon = 0.3595, game_rewardavg20 = 0.0351\n",
            "1\n",
            "11\n",
            "45\n",
            "4\n",
            "5\n",
            "8\n",
            "1\n",
            "19\n",
            "2\n",
            "15\n",
            "11\n",
            "48\n",
            "2\n",
            "28\n",
            "31\n",
            "1\n",
            "6\n",
            "18\n",
            "43\n",
            "7\n",
            "GOOD Q (left to the green): 0.91537153886508 , BAD Q (under the red) 0.007880506146660249 , BAD Q 2(left of the red) 0.05669631596246738\n",
            "GOOD V (left to the green): 0.91537153886508 , BAD V (under the red) 0.05669631596246738\n",
            "MSE_value= tensor(0.2219, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.01,0.72)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.99)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.52,4.16)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.60,-1.82)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.31,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.47,1.90,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.35,6.42,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.53,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2219, dtype=torch.float64)\n",
            "GAME = 1200, LOSSavg20 = 0.06151732, number of steps done in last= 7, last_episode's_last_state = 11, epsilon = 0.3495, game_rewardavg20 = 0.0338\n",
            "3\n",
            "6\n",
            "8\n",
            "12\n",
            "10\n",
            "31\n",
            "62\n",
            "37\n",
            "25\n",
            "6\n",
            "11\n",
            "20\n",
            "2\n",
            "1\n",
            "3\n",
            "10\n",
            "2\n",
            "9\n",
            "12\n",
            "78\n",
            "GOOD Q (left to the green): 0.9110310013305912 , BAD Q (under the red) 0.059381482737242475 , BAD Q 2(left of the red) 0.08409394100331918\n",
            "GOOD V (left to the green): 0.9110310013305912 , BAD V (under the red) 0.08409394100331918\n",
            "MSE_value= tensor(0.2054, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.01,0.74)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.98)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.52,4.15)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.58,-1.85)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.30,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.48,1.89,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.41,6.44,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.94,-5.52,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2054, dtype=torch.float64)\n",
            "GAME = 1220, LOSSavg20 = 0.06201728, number of steps done in last= 78, last_episode's_last_state = 7, epsilon = 0.3395, game_rewardavg20 = 0.0392\n",
            "6\n",
            "2\n",
            "16\n",
            "5\n",
            "5\n",
            "1\n",
            "8\n",
            "32\n",
            "34\n",
            "9\n",
            "5\n",
            "1\n",
            "51\n",
            "10\n",
            "4\n",
            "32\n",
            "5\n",
            "10\n",
            "2\n",
            "9\n",
            "GOOD Q (left to the green): 0.9003927770495846 , BAD Q (under the red) 0.013960027096924399 , BAD Q 2(left of the red) 0.10840152815140947\n",
            "GOOD V (left to the green): 0.9003927770495846 , BAD V (under the red) 0.10840152815140947\n",
            "MSE_value= tensor(0.2113, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.01,0.73)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-1.98)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.52,4.14)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.60,-1.83)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.31,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.49,1.88,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.41,6.45,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.92,-5.54,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2113, dtype=torch.float64)\n",
            "GAME = 1240, LOSSavg20 = 0.06159610, number of steps done in last= 9, last_episode's_last_state = 11, epsilon = 0.3295, game_rewardavg20 = 0.0411\n",
            "2\n",
            "7\n",
            "7\n",
            "4\n",
            "4\n",
            "40\n",
            "4\n",
            "7\n",
            "7\n",
            "13\n",
            "6\n",
            "21\n",
            "10\n",
            "1\n",
            "1\n",
            "7\n",
            "32\n",
            "49\n",
            "3\n",
            "44\n",
            "GOOD Q (left to the green): 0.8956588512022894 , BAD Q (under the red) -0.06946655946143193 , BAD Q 2(left of the red) 0.08642870044179862\n",
            "GOOD V (left to the green): 0.8956588512022894 , BAD V (under the red) 0.08642870044179862\n",
            "MSE_value= tensor(0.2266, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.71)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.53,4.15)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.58,-1.85)\n",
            "\n",
            "────────────╭●─────────Rot(0.04,-1.32,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.48,1.90,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.37,6.43,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.95,-5.51,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2266, dtype=torch.float64)\n",
            "GAME = 1260, LOSSavg20 = 0.06111350, number of steps done in last= 44, last_episode's_last_state = 7, epsilon = 0.3195, game_rewardavg20 = 0.0441\n",
            "8\n",
            "11\n",
            "5\n",
            "10\n",
            "15\n",
            "14\n",
            "13\n",
            "3\n",
            "36\n",
            "23\n",
            "79\n",
            "3\n",
            "6\n",
            "1\n",
            "46\n",
            "41\n",
            "50\n",
            "3\n",
            "59\n",
            "5\n",
            "GOOD Q (left to the green): 0.8772212377331051 , BAD Q (under the red) -0.13307044557508046 , BAD Q 2(left of the red) 0.09677021584668899\n",
            "GOOD V (left to the green): 0.8772212377331051 , BAD V (under the red) 0.11334277654989527\n",
            "MSE_value= tensor(0.2161, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.05,0.70)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.52,4.15)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.54,-1.88)\n",
            "\n",
            "────────────╭●─────────Rot(0.03,-1.34,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.46,1.90,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.33,6.43,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.97,-5.48,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.2161, dtype=torch.float64)\n",
            "GAME = 1280, LOSSavg20 = 0.06077894, number of steps done in last= 5, last_episode's_last_state = 7, epsilon = 0.3095, game_rewardavg20 = 0.0424\n",
            "3\n",
            "5\n",
            "38\n",
            "45\n",
            "6\n",
            "2\n",
            "4\n",
            "12\n",
            "11\n",
            "1\n",
            "10\n",
            "11\n",
            "23\n",
            "10\n",
            "1\n",
            "7\n",
            "1\n",
            "79\n",
            "59\n",
            "2\n",
            "GOOD Q (left to the green): 0.9042303469997525 , BAD Q (under the red) -0.0029075112222738397 , BAD Q 2(left of the red) 0.07788110282128291\n",
            "GOOD V (left to the green): 0.9042303469997525 , BAD V (under the red) 0.08812858574244242\n",
            "MSE_value= tensor(0.1961, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.02,0.73)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.50,4.15)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.54,-1.88)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.31,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.44,1.90,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.34,6.42,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.97,-5.48,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1961, dtype=torch.float64)\n",
            "GAME = 1300, LOSSavg20 = 0.06105952, number of steps done in last= 2, last_episode's_last_state = 11, epsilon = 0.2995, game_rewardavg20 = 0.0414\n",
            "71\n",
            "12\n",
            "7\n",
            "2\n",
            "3\n",
            "12\n",
            "1\n",
            "10\n",
            "6\n",
            "67\n",
            "34\n",
            "15\n",
            "57\n",
            "13\n",
            "25\n",
            "35\n",
            "5\n",
            "50\n",
            "28\n",
            "5\n",
            "GOOD Q (left to the green): 0.9080514772219341 , BAD Q (under the red) 0.07453981481379901 , BAD Q 2(left of the red) 0.10437489085585827\n",
            "GOOD V (left to the green): 0.9080514772219341 , BAD V (under the red) 0.10437489085585827\n",
            "MSE_value= tensor(0.1842, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.02,0.78)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.98)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.50,4.14)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.54,-1.89)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.29,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.43,1.89,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.37,6.43,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.98,-5.48,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1842, dtype=torch.float64)\n",
            "GAME = 1320, LOSSavg20 = 0.06077163, number of steps done in last= 5, last_episode's_last_state = 7, epsilon = 0.2895, game_rewardavg20 = 0.0421\n",
            "3\n",
            "6\n",
            "55\n",
            "1\n",
            "6\n",
            "3\n",
            "1\n",
            "1\n",
            "5\n",
            "1\n",
            "15\n",
            "16\n",
            "2\n",
            "1\n",
            "4\n",
            "15\n",
            "3\n",
            "37\n",
            "12\n",
            "2\n",
            "GOOD Q (left to the green): 0.912495652413507 , BAD Q (under the red) 0.05558575600328042 , BAD Q 2(left of the red) 0.1483545976095882\n",
            "GOOD V (left to the green): 0.912495652413507 , BAD V (under the red) 0.1483545976095882\n",
            "MSE_value= tensor(0.1826, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.02,0.77)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-1.98)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.49,4.12)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.55,-1.87)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.30,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.45,1.87,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.38,6.44,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.96,-5.49,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1826, dtype=torch.float64)\n",
            "GAME = 1340, LOSSavg20 = 0.06050699, number of steps done in last= 2, last_episode's_last_state = 7, epsilon = 0.2795, game_rewardavg20 = 0.0444\n",
            "6\n",
            "28\n",
            "5\n",
            "2\n",
            "9\n",
            "19\n",
            "28\n",
            "10\n",
            "4\n",
            "25\n",
            "3\n",
            "2\n",
            "12\n",
            "2\n",
            "6\n",
            "5\n",
            "2\n",
            "12\n",
            "76\n",
            "6\n",
            "GOOD Q (left to the green): 0.8989985134815746 , BAD Q (under the red) -0.017410537136062276 , BAD Q 2(left of the red) 0.15451521202817742\n",
            "GOOD V (left to the green): 0.8989985134815746 , BAD V (under the red) 0.15451521202817742\n",
            "MSE_value= tensor(0.1857, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.76)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.47,4.11)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.54,-1.88)\n",
            "\n",
            "────────────╭●─────────Rot(0.05,-1.31,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.47,1.87,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.40,6.47,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.95,-5.49,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1857, dtype=torch.float64)\n",
            "GAME = 1360, LOSSavg20 = 0.06079936, number of steps done in last= 6, last_episode's_last_state = 11, epsilon = 0.2695, game_rewardavg20 = 0.0411\n",
            "4\n",
            "12\n",
            "11\n",
            "47\n",
            "5\n",
            "12\n",
            "5\n",
            "2\n",
            "25\n",
            "2\n",
            "13\n",
            "8\n",
            "19\n",
            "2\n",
            "12\n",
            "30\n",
            "37\n",
            "8\n",
            "30\n",
            "3\n",
            "GOOD Q (left to the green): 0.8798559348298678 , BAD Q (under the red) -0.15012732154222608 , BAD Q 2(left of the red) 0.1391357994891989\n",
            "GOOD V (left to the green): 0.8798559348298678 , BAD V (under the red) 0.1391357994891989\n",
            "MSE_value= tensor(0.1994, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.05,0.73)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.03)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.45,4.13)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.57,-1.85)\n",
            "\n",
            "────────────╭●─────────Rot(0.03,-1.34,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.46,1.89,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.41,6.46,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.52,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1994, dtype=torch.float64)\n",
            "GAME = 1380, LOSSavg20 = 0.06089562, number of steps done in last= 3, last_episode's_last_state = 11, epsilon = 0.2595, game_rewardavg20 = 0.0350\n",
            "5\n",
            "49\n",
            "26\n",
            "14\n",
            "26\n",
            "4\n",
            "14\n",
            "4\n",
            "36\n",
            "7\n",
            "53\n",
            "3\n",
            "1\n",
            "2\n",
            "10\n",
            "16\n",
            "71\n",
            "5\n",
            "3\n",
            "28\n",
            "GOOD Q (left to the green): 0.9023190406166419 , BAD Q (under the red) -0.16660148870626157 , BAD Q 2(left of the red) 0.12591264902994925\n",
            "GOOD V (left to the green): 0.9023190406166419 , BAD V (under the red) 0.12591264902994925\n",
            "MSE_value= tensor(0.1987, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.05,0.72)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.03)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.12)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.55,-1.88)\n",
            "\n",
            "────────────╭●─────────Rot(0.02,-1.35,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.44,1.90,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.44,6.49,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.94,-5.50,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1987, dtype=torch.float64)\n",
            "GAME = 1400, LOSSavg20 = 0.06097893, number of steps done in last= 28, last_episode's_last_state = 11, epsilon = 0.2495, game_rewardavg20 = 0.0322\n",
            "70\n",
            "1\n",
            "38\n",
            "42\n",
            "34\n",
            "193\n",
            "5\n",
            "33\n",
            "43\n",
            "6\n",
            "44\n",
            "2\n",
            "7\n",
            "57\n",
            "10\n",
            "11\n",
            "29\n",
            "4\n",
            "25\n",
            "16\n",
            "GOOD Q (left to the green): 0.9446130823441012 , BAD Q (under the red) 0.07268939158218579 , BAD Q 2(left of the red) 0.1372209525381357\n",
            "GOOD V (left to the green): 0.9446130823441012 , BAD V (under the red) 0.1372209525381357\n",
            "MSE_value= tensor(0.1709, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.01,0.78)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-1.99)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.11)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.57,-1.86)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.29,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.43,1.89,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.42,6.52,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.92,-5.53,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1709, dtype=torch.float64)\n",
            "GAME = 1420, LOSSavg20 = 0.06078890, number of steps done in last= 16, last_episode's_last_state = 7, epsilon = 0.2395, game_rewardavg20 = 0.0314\n",
            "13\n",
            "20\n",
            "56\n",
            "7\n",
            "5\n",
            "34\n",
            "2\n",
            "1\n",
            "42\n",
            "22\n",
            "1\n",
            "37\n",
            "8\n",
            "2\n",
            "4\n",
            "5\n",
            "15\n",
            "18\n",
            "4\n",
            "7\n",
            "GOOD Q (left to the green): 0.958396312440399 , BAD Q (under the red) 0.12099800600268257 , BAD Q 2(left of the red) 0.1735961402216606\n",
            "GOOD V (left to the green): 0.958396312440399 , BAD V (under the red) 0.1735961402216606\n",
            "MSE_value= tensor(0.1572, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.00,0.79)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-1.98)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.43,4.09)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.58,-1.85)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.27,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.44,1.87,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.45,6.53,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.90,-5.55,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1572, dtype=torch.float64)\n",
            "GAME = 1440, LOSSavg20 = 0.06109038, number of steps done in last= 7, last_episode's_last_state = 11, epsilon = 0.2295, game_rewardavg20 = 0.0306\n",
            "37\n",
            "26\n",
            "42\n",
            "3\n",
            "6\n",
            "33\n",
            "21\n",
            "12\n",
            "35\n",
            "23\n",
            "4\n",
            "12\n",
            "1\n",
            "4\n",
            "37\n",
            "1\n",
            "1\n",
            "6\n",
            "7\n",
            "12\n",
            "GOOD Q (left to the green): 0.9183024924816026 , BAD Q (under the red) 0.07812945737246618 , BAD Q 2(left of the red) 0.12018560395501732\n",
            "GOOD V (left to the green): 0.9183024924816026 , BAD V (under the red) 0.12018560395501732\n",
            "MSE_value= tensor(0.1736, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.79)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.99)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.45,4.11)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.56,-1.87)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.28,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.43,1.89,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.44,6.53,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.92,-5.53,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1736, dtype=torch.float64)\n",
            "GAME = 1460, LOSSavg20 = 0.06063111, number of steps done in last= 12, last_episode's_last_state = 7, epsilon = 0.2195, game_rewardavg20 = 0.0307\n",
            "21\n",
            "15\n",
            "12\n",
            "2\n",
            "28\n",
            "4\n",
            "33\n",
            "22\n",
            "2\n",
            "49\n",
            "7\n",
            "15\n",
            "12\n",
            "18\n",
            "38\n",
            "41\n",
            "48\n",
            "12\n",
            "1\n",
            "6\n",
            "GOOD Q (left to the green): 0.8917033337847303 , BAD Q (under the red) 0.020401342089736263 , BAD Q 2(left of the red) 0.13999613686171464\n",
            "GOOD V (left to the green): 0.8917033337847303 , BAD V (under the red) 0.13999613686171464\n",
            "MSE_value= tensor(0.1686, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.05,0.79)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.47,4.11)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.50,-1.92)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.29,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.43,1.89,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.42,6.53,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.97,-5.47,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1686, dtype=torch.float64)\n",
            "GAME = 1480, LOSSavg20 = 0.06090110, number of steps done in last= 6, last_episode's_last_state = 11, epsilon = 0.2095, game_rewardavg20 = 0.0322\n",
            "2\n",
            "79\n",
            "4\n",
            "45\n",
            "1\n",
            "11\n",
            "5\n",
            "28\n",
            "6\n",
            "53\n",
            "31\n",
            "11\n",
            "41\n",
            "1\n",
            "51\n",
            "6\n",
            "11\n",
            "11\n",
            "3\n",
            "4\n",
            "GOOD Q (left to the green): 0.9096422210715815 , BAD Q (under the red) 0.15609443436117615 , BAD Q 2(left of the red) 0.18421747382174808\n",
            "GOOD V (left to the green): 0.9096422210715815 , BAD V (under the red) 0.18421747382174808\n",
            "MSE_value= tensor(0.1417, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.02,0.82)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.96)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.46,4.08)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.50,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.26,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.45,1.86,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.44,6.56,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.97,-5.47,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1417, dtype=torch.float64)\n",
            "GAME = 1500, LOSSavg20 = 0.06114766, number of steps done in last= 4, last_episode's_last_state = 7, epsilon = 0.1995, game_rewardavg20 = 0.0354\n",
            "36\n",
            "25\n",
            "3\n",
            "6\n",
            "16\n",
            "14\n",
            "11\n",
            "1\n",
            "14\n",
            "39\n",
            "24\n",
            "8\n",
            "2\n",
            "3\n",
            "12\n",
            "30\n",
            "9\n",
            "1\n",
            "1\n",
            "42\n",
            "GOOD Q (left to the green): 0.8847360722979808 , BAD Q (under the red) 0.11096445437594377 , BAD Q 2(left of the red) 0.22776268656000143\n",
            "GOOD V (left to the green): 0.8847360722979808 , BAD V (under the red) 0.22776268656000143\n",
            "MSE_value= tensor(0.1402, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.03,0.82)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-1.95)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.46,4.07)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.50,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.28,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.48,1.84,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.48,6.56,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.97,-5.48,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1402, dtype=torch.float64)\n",
            "GAME = 1520, LOSSavg20 = 0.06088313, number of steps done in last= 42, last_episode's_last_state = 7, epsilon = 0.1895, game_rewardavg20 = 0.0400\n",
            "4\n",
            "2\n",
            "2\n",
            "23\n",
            "1\n",
            "42\n",
            "28\n",
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            "7\n",
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            "2\n",
            "44\n",
            "2\n",
            "5\n",
            "13\n",
            "42\n",
            "1\n",
            "3\n",
            "GOOD Q (left to the green): 0.8761001017836366 , BAD Q (under the red) 0.05853929205297048 , BAD Q 2(left of the red) 0.21292995355738265\n",
            "GOOD V (left to the green): 0.8761001017836366 , BAD V (under the red) 0.21292995355738265\n",
            "MSE_value= tensor(0.1521, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.04,0.81)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-1.96)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.45,4.07)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.51,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.29,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.48,1.85,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.47,6.57,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.96,-5.49,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1521, dtype=torch.float64)\n",
            "GAME = 1540, LOSSavg20 = 0.06085937, number of steps done in last= 3, last_episode's_last_state = 11, epsilon = 0.1795, game_rewardavg20 = 0.0373\n",
            "2\n",
            "19\n",
            "2\n",
            "6\n",
            "52\n",
            "52\n",
            "2\n",
            "5\n",
            "3\n",
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            "16\n",
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            "27\n",
            "11\n",
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            "9\n",
            "43\n",
            "2\n",
            "20\n",
            "24\n",
            "GOOD Q (left to the green): 0.8777294488821771 , BAD Q (under the red) 0.06659412105370321 , BAD Q 2(left of the red) 0.23382134767203466\n",
            "GOOD V (left to the green): 0.8777294488821771 , BAD V (under the red) 0.23382134767203466\n",
            "MSE_value= tensor(0.1419, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.04,0.81)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-1.96)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.46,4.06)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.50,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.29,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.51,1.84,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.43,6.57,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.97,-5.48,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1419, dtype=torch.float64)\n",
            "GAME = 1560, LOSSavg20 = 0.06096500, number of steps done in last= 24, last_episode's_last_state = 7, epsilon = 0.1695, game_rewardavg20 = 0.0362\n",
            "4\n",
            "20\n",
            "8\n",
            "7\n",
            "3\n",
            "15\n",
            "6\n",
            "12\n",
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            "10\n",
            "30\n",
            "16\n",
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            "1\n",
            "29\n",
            "13\n",
            "24\n",
            "1\n",
            "7\n",
            "38\n",
            "GOOD Q (left to the green): 0.8744828273462589 , BAD Q (under the red) 0.018758596624007084 , BAD Q 2(left of the red) 0.20604058153898652\n",
            "GOOD V (left to the green): 0.8744828273462589 , BAD V (under the red) 0.20604058153898652\n",
            "MSE_value= tensor(0.1470, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.05,0.80)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-1.97)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.46,4.06)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.49,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.30,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.52,1.85,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.39,6.56,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.99,-5.46,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1470, dtype=torch.float64)\n",
            "GAME = 1580, LOSSavg20 = 0.06116387, number of steps done in last= 38, last_episode's_last_state = 7, epsilon = 0.1595, game_rewardavg20 = 0.0362\n",
            "3\n",
            "10\n",
            "1\n",
            "30\n",
            "20\n",
            "3\n",
            "13\n",
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            "24\n",
            "50\n",
            "76\n",
            "4\n",
            "18\n",
            "2\n",
            "36\n",
            "19\n",
            "1\n",
            "4\n",
            "15\n",
            "32\n",
            "GOOD Q (left to the green): 0.8653145576033987 , BAD Q (under the red) -0.1022920078826533 , BAD Q 2(left of the red) 0.14973797064346445\n",
            "GOOD V (left to the green): 0.8653145576033987 , BAD V (under the red) 0.14973797064346445\n",
            "MSE_value= tensor(0.1664, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.08,0.77)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.01)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.47,4.08)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.48,-1.96)\n",
            "\n",
            "────────────╭●─────────Rot(0.04,-1.33,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.48,1.89,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.35,6.56,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.99,-5.46,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1664, dtype=torch.float64)\n",
            "GAME = 1600, LOSSavg20 = 0.06167604, number of steps done in last= 32, last_episode's_last_state = 11, epsilon = 0.1495, game_rewardavg20 = 0.0349\n",
            "38\n",
            "19\n",
            "5\n",
            "7\n",
            "9\n",
            "56\n",
            "6\n",
            "7\n",
            "11\n",
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            "6\n",
            "40\n",
            "14\n",
            "17\n",
            "11\n",
            "16\n",
            "4\n",
            "3\n",
            "2\n",
            "43\n",
            "GOOD Q (left to the green): 0.8873890561576359 , BAD Q (under the red) -0.03529791878819022 , BAD Q 2(left of the red) 0.17063741849696346\n",
            "GOOD V (left to the green): 0.8873890561576359 , BAD V (under the red) 0.17063741849696346\n",
            "MSE_value= tensor(0.1673, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.07,0.79)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.46,4.07)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.50,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.31,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.47,1.88,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.35,6.56,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.97,-5.48,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1673, dtype=torch.float64)\n",
            "GAME = 1620, LOSSavg20 = 0.06246860, number of steps done in last= 43, last_episode's_last_state = 7, epsilon = 0.1395, game_rewardavg20 = 0.0310\n",
            "29\n",
            "6\n",
            "17\n",
            "6\n",
            "17\n",
            "71\n",
            "68\n",
            "6\n",
            "4\n",
            "62\n",
            "21\n",
            "1\n",
            "7\n",
            "11\n",
            "44\n",
            "13\n",
            "45\n",
            "43\n",
            "18\n",
            "78\n",
            "GOOD Q (left to the green): 0.8786563115009529 , BAD Q (under the red) 0.0016969637156458717 , BAD Q 2(left of the red) 0.1368213225252249\n",
            "GOOD V (left to the green): 0.8786563115009529 , BAD V (under the red) 0.1368213225252249\n",
            "MSE_value= tensor(0.1703, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.07,0.80)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.46,4.09)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.52,-1.92)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.30,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.45,1.89,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.35,6.58,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.94,-5.50,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1703, dtype=torch.float64)\n",
            "GAME = 1640, LOSSavg20 = 0.06261083, number of steps done in last= 78, last_episode's_last_state = 11, epsilon = 0.1295, game_rewardavg20 = 0.0288\n",
            "2\n",
            "4\n",
            "8\n",
            "5\n",
            "32\n",
            "4\n",
            "2\n",
            "48\n",
            "23\n",
            "4\n",
            "4\n",
            "136\n",
            "4\n",
            "7\n",
            "24\n",
            "30\n",
            "62\n",
            "1\n",
            "8\n",
            "76\n",
            "GOOD Q (left to the green): 0.8802819188337431 , BAD Q (under the red) 0.028503665644953602 , BAD Q 2(left of the red) 0.11564104730939972\n",
            "GOOD V (left to the green): 0.8802819188337431 , BAD V (under the red) 0.11564104730939972\n",
            "MSE_value= tensor(0.1623, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.06,0.81)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.43,4.09)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.52,-1.92)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.29,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.43,1.89,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.35,6.60,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.51,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1623, dtype=torch.float64)\n",
            "GAME = 1660, LOSSavg20 = 0.06279197, number of steps done in last= 76, last_episode's_last_state = 7, epsilon = 0.1195, game_rewardavg20 = 0.0277\n",
            "25\n",
            "11\n",
            "12\n",
            "1\n",
            "14\n",
            "7\n",
            "49\n",
            "5\n",
            "4\n",
            "6\n",
            "37\n",
            "2\n",
            "2\n",
            "14\n",
            "2\n",
            "2\n",
            "1\n",
            "4\n",
            "1\n",
            "14\n",
            "GOOD Q (left to the green): 0.8608788508843255 , BAD Q (under the red) 0.04422781499619244 , BAD Q 2(left of the red) 0.15868371219506805\n",
            "GOOD V (left to the green): 0.8608788508843255 , BAD V (under the red) 0.15868371219506805\n",
            "MSE_value= tensor(0.1639, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.07,0.82)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.99)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.46,4.08)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.53,-1.91)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.29,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.44,1.87,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.35,6.60,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.51,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1639, dtype=torch.float64)\n",
            "GAME = 1680, LOSSavg20 = 0.06318644, number of steps done in last= 14, last_episode's_last_state = 11, epsilon = 0.1095, game_rewardavg20 = 0.0247\n",
            "16\n",
            "1\n",
            "12\n",
            "2\n",
            "8\n",
            "9\n",
            "2\n",
            "13\n",
            "11\n",
            "6\n",
            "3\n",
            "19\n",
            "19\n",
            "11\n",
            "62\n",
            "2\n",
            "3\n",
            "23\n",
            "16\n",
            "3\n",
            "GOOD Q (left to the green): 0.8859427642184168 , BAD Q (under the red) 0.039288920183599974 , BAD Q 2(left of the red) 0.19068728692112236\n",
            "GOOD V (left to the green): 0.8859427642184168 , BAD V (under the red) 0.19068728692112236\n",
            "MSE_value= tensor(0.1556, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.06,0.82)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-1.98)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.05)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.53,-1.91)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.29,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.46,1.87,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.35,6.61,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.92,-5.52,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1556, dtype=torch.float64)\n",
            "GAME = 1700, LOSSavg20 = 0.06301743, number of steps done in last= 3, last_episode's_last_state = 7, epsilon = 0.0995, game_rewardavg20 = 0.0262\n",
            "4\n",
            "3\n",
            "55\n",
            "82\n",
            "4\n",
            "11\n",
            "8\n",
            "16\n",
            "46\n",
            "9\n",
            "1\n",
            "2\n",
            "10\n",
            "31\n",
            "29\n",
            "20\n",
            "1\n",
            "26\n",
            "8\n",
            "8\n",
            "GOOD Q (left to the green): 0.865956185583908 , BAD Q (under the red) -0.014927271932640207 , BAD Q 2(left of the red) 0.18483755309468486\n",
            "GOOD V (left to the green): 0.865956185583908 , BAD V (under the red) 0.18483755309468486\n",
            "MSE_value= tensor(0.1623, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.08,0.81)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.06)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.52,-1.92)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.30,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.45,1.87,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.33,6.61,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.51,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1623, dtype=torch.float64)\n",
            "GAME = 1720, LOSSavg20 = 0.06324944, number of steps done in last= 8, last_episode's_last_state = 7, epsilon = 0.0895, game_rewardavg20 = 0.0251\n",
            "62\n",
            "10\n",
            "5\n",
            "1\n",
            "5\n",
            "25\n",
            "9\n",
            "71\n",
            "26\n",
            "1\n",
            "44\n",
            "1\n",
            "28\n",
            "1\n",
            "19\n",
            "8\n",
            "14\n",
            "2\n",
            "9\n",
            "39\n",
            "GOOD Q (left to the green): 0.866798247620921 , BAD Q (under the red) 0.01193684126882466 , BAD Q 2(left of the red) 0.19725569939373305\n",
            "GOOD V (left to the green): 0.866798247620921 , BAD V (under the red) 0.19725569939373305\n",
            "MSE_value= tensor(0.1461, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.07,0.82)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-1.99)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.05)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.49,-1.96)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.30,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.46,1.86,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.29,6.62,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.96,-5.48,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1461, dtype=torch.float64)\n",
            "GAME = 1740, LOSSavg20 = 0.06341662, number of steps done in last= 39, last_episode's_last_state = 7, epsilon = 0.0795, game_rewardavg20 = 0.0225\n",
            "16\n",
            "30\n",
            "7\n",
            "1\n",
            "29\n",
            "12\n",
            "42\n",
            "1\n",
            "18\n",
            "2\n",
            "19\n",
            "2\n",
            "26\n",
            "8\n",
            "13\n",
            "14\n",
            "7\n",
            "3\n",
            "35\n",
            "28\n",
            "GOOD Q (left to the green): 0.8878418556361255 , BAD Q (under the red) 0.07217027093989237 , BAD Q 2(left of the red) 0.19230391603801417\n",
            "GOOD V (left to the green): 0.8878418556361255 , BAD V (under the red) 0.19230391603801417\n",
            "MSE_value= tensor(0.1388, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.06,0.84)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.98)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.42,4.05)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.51,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.28,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.46,1.86,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.28,6.61,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.94,-5.50,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1388, dtype=torch.float64)\n",
            "GAME = 1760, LOSSavg20 = 0.06387094, number of steps done in last= 28, last_episode's_last_state = 11, epsilon = 0.0695, game_rewardavg20 = 0.0210\n",
            "68\n",
            "82\n",
            "1\n",
            "1\n",
            "4\n",
            "47\n",
            "1\n",
            "8\n",
            "13\n",
            "28\n",
            "27\n",
            "37\n",
            "20\n",
            "29\n",
            "13\n",
            "23\n",
            "62\n",
            "6\n",
            "9\n",
            "5\n",
            "GOOD Q (left to the green): 0.8771816779110536 , BAD Q (under the red) 0.03172441198957715 , BAD Q 2(left of the red) 0.2620009336403465\n",
            "GOOD V (left to the green): 0.8771816779110536 , BAD V (under the red) 0.2620009336403465\n",
            "MSE_value= tensor(0.1229, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.06,0.83)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-1.98)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.42,4.01)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.49,-1.96)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.29,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.49,1.84,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.25,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.96,-5.49,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1229, dtype=torch.float64)\n",
            "GAME = 1780, LOSSavg20 = 0.06434232, number of steps done in last= 5, last_episode's_last_state = 7, epsilon = 0.0595, game_rewardavg20 = 0.0220\n",
            "35\n",
            "63\n",
            "34\n",
            "12\n",
            "13\n",
            "25\n",
            "1\n",
            "6\n",
            "39\n",
            "13\n",
            "13\n",
            "7\n",
            "12\n",
            "4\n",
            "7\n",
            "1\n",
            "18\n",
            "1\n",
            "17\n",
            "9\n",
            "GOOD Q (left to the green): 0.8649258741424146 , BAD Q (under the red) 0.09000024392930334 , BAD Q 2(left of the red) 0.2852775629322683\n",
            "GOOD V (left to the green): 0.8649258741424146 , BAD V (under the red) 0.2852775629322683\n",
            "MSE_value= tensor(0.1104, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.06,0.84)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-1.96)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.43,4.00)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.47,-1.98)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.28,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.51,1.82,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.23,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.97,-5.47,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1104, dtype=torch.float64)\n",
            "GAME = 1800, LOSSavg20 = 0.06476114, number of steps done in last= 9, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = 0.0203\n",
            "20\n",
            "20\n",
            "41\n",
            "11\n",
            "1\n",
            "27\n",
            "4\n",
            "102\n",
            "9\n",
            "29\n",
            "1\n",
            "28\n",
            "11\n",
            "20\n",
            "30\n",
            "4\n",
            "2\n",
            "8\n",
            "7\n",
            "7\n",
            "GOOD Q (left to the green): 0.8329030296305522 , BAD Q (under the red) 0.15522867675162338 , BAD Q 2(left of the red) 0.2637879469523846\n",
            "GOOD V (left to the green): 0.8329030296305522 , BAD V (under the red) 0.2637879469523846\n",
            "MSE_value= tensor(0.1122, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.07,0.88)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-1.94)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.43,4.01)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.48,-1.96)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.26,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.51,1.81,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.18,6.63,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.97,-5.48,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1122, dtype=torch.float64)\n",
            "GAME = 1820, LOSSavg20 = 0.06486804, number of steps done in last= 7, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = 0.0172\n",
            "3\n",
            "40\n",
            "12\n",
            "30\n",
            "5\n",
            "6\n",
            "7\n",
            "5\n",
            "11\n",
            "5\n",
            "5\n",
            "2\n",
            "30\n",
            "57\n",
            "28\n",
            "40\n",
            "3\n",
            "20\n",
            "13\n",
            "2\n",
            "GOOD Q (left to the green): 0.8025545411620902 , BAD Q (under the red) 0.10968388401221113 , BAD Q 2(left of the red) 0.1969908149484318\n",
            "GOOD V (left to the green): 0.8025545411620902 , BAD V (under the red) 0.1969908149484318\n",
            "MSE_value= tensor(0.1335, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.09,0.89)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-1.95)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.43,4.04)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.49,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.27,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.48,1.84,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.13,6.61,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.95,-5.48,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1335, dtype=torch.float64)\n",
            "GAME = 1840, LOSSavg20 = 0.06508002, number of steps done in last= 2, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = 0.0147\n",
            "20\n",
            "47\n",
            "21\n",
            "16\n",
            "17\n",
            "4\n",
            "3\n",
            "2\n",
            "3\n",
            "32\n",
            "13\n",
            "16\n",
            "6\n",
            "22\n",
            "15\n",
            "7\n",
            "5\n",
            "5\n",
            "10\n",
            "14\n",
            "GOOD Q (left to the green): 0.7873520983672528 , BAD Q (under the red) 0.04671229423851958 , BAD Q 2(left of the red) 0.15567442535567388\n",
            "GOOD V (left to the green): 0.7873520983672528 , BAD V (under the red) 0.15567442535567388\n",
            "MSE_value= tensor(0.1354, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.11,0.88)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.86,-1.98)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.06)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.46,-1.97)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.29,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.46,1.86,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.11,6.62,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.98,-5.45,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1354, dtype=torch.float64)\n",
            "GAME = 1860, LOSSavg20 = 0.06537858, number of steps done in last= 14, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = 0.0122\n",
            "44\n",
            "26\n",
            "4\n",
            "112\n",
            "3\n",
            "5\n",
            "12\n",
            "24\n",
            "2\n",
            "3\n",
            "15\n",
            "17\n",
            "4\n",
            "37\n",
            "35\n",
            "4\n",
            "1\n",
            "35\n",
            "3\n",
            "9\n",
            "GOOD Q (left to the green): 0.7877491658734237 , BAD Q (under the red) 0.09349460056038394 , BAD Q 2(left of the red) 0.19305590401006573\n",
            "GOOD V (left to the green): 0.7877491658734237 , BAD V (under the red) 0.19305590401006573\n",
            "MSE_value= tensor(0.1285, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.11,0.91)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.87,-1.96)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.05)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.46,-1.97)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.28,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.45,1.84,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.08,6.60,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.98,-5.45,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1285, dtype=torch.float64)\n",
            "GAME = 1880, LOSSavg20 = 0.06613992, number of steps done in last= 9, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = 0.0137\n",
            "66\n",
            "16\n",
            "1\n",
            "3\n",
            "32\n",
            "72\n",
            "69\n",
            "65\n",
            "10\n",
            "3\n",
            "3\n",
            "28\n",
            "19\n",
            "27\n",
            "20\n",
            "18\n",
            "15\n",
            "10\n",
            "27\n",
            "6\n",
            "GOOD Q (left to the green): 0.8091558534286789 , BAD Q (under the red) 0.11050048032862919 , BAD Q 2(left of the red) 0.22338873663595443\n",
            "GOOD V (left to the green): 0.8091558534286789 , BAD V (under the red) 0.22338873663595443\n",
            "MSE_value= tensor(0.1310, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.10,0.92)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.95)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.43,4.03)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.48,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.28,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.46,1.83,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.05,6.59,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.95,-5.47,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1310, dtype=torch.float64)\n",
            "GAME = 1900, LOSSavg20 = 0.06639021, number of steps done in last= 6, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = 0.0137\n",
            "13\n",
            "26\n",
            "2\n",
            "10\n",
            "18\n",
            "3\n",
            "23\n",
            "8\n",
            "31\n",
            "1\n",
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            "21\n",
            "3\n",
            "12\n",
            "14\n",
            "8\n",
            "25\n",
            "1\n",
            "14\n",
            "1\n",
            "GOOD Q (left to the green): 0.8085741325812579 , BAD Q (under the red) 0.1399402052990376 , BAD Q 2(left of the red) 0.24225457336802042\n",
            "GOOD V (left to the green): 0.8085741325812579 , BAD V (under the red) 0.24225457336802042\n",
            "MSE_value= tensor(0.1240, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.09,0.92)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.94)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.02)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.48,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.27,0.09)──────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.46,1.82,-0.10)───────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(0.03,6.60,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.95,-5.47,0.31)─┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1240, dtype=torch.float64)\n",
            "GAME = 1920, LOSSavg20 = 0.06643161, number of steps done in last= 1, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = 0.0125\n",
            "33\n",
            "8\n",
            "41\n",
            "4\n",
            "1\n",
            "5\n",
            "3\n",
            "30\n",
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            "22\n",
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            "71\n",
            "13\n",
            "33\n",
            "1\n",
            "40\n",
            "14\n",
            "15\n",
            "GOOD Q (left to the green): 0.8255084967475086 , BAD Q (under the red) 0.11230595886641076 , BAD Q 2(left of the red) 0.1766738549938155\n",
            "GOOD V (left to the green): 0.8255084967475086 , BAD V (under the red) 0.1766738549938155\n",
            "MSE_value= tensor(0.1295, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.10,0.92)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.87,-1.96)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.04)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.47,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.43,1.86,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.01,6.60,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.96,-5.45,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1295, dtype=torch.float64)\n",
            "GAME = 1940, LOSSavg20 = 0.06635855, number of steps done in last= 15, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = 0.0122\n",
            "52\n",
            "3\n",
            "26\n",
            "11\n",
            "10\n",
            "22\n",
            "42\n",
            "3\n",
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            "66\n",
            "25\n",
            "27\n",
            "2\n",
            "15\n",
            "3\n",
            "55\n",
            "15\n",
            "3\n",
            "73\n",
            "3\n",
            "GOOD Q (left to the green): 0.8552159072193513 , BAD Q (under the red) 0.10339077861174978 , BAD Q 2(left of the red) 0.20470567677106832\n",
            "GOOD V (left to the green): 0.8552159072193513 , BAD V (under the red) 0.20470567677106832\n",
            "MSE_value= tensor(0.1292, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.10,0.92)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.97)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.46,4.02)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.46,-1.96)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.87,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.03,6.59,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.97,-5.45,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1292, dtype=torch.float64)\n",
            "GAME = 1960, LOSSavg20 = 0.06647922, number of steps done in last= 3, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = 0.0102\n",
            "22\n",
            "8\n",
            "15\n",
            "30\n",
            "5\n",
            "49\n",
            "43\n",
            "21\n",
            "3\n",
            "22\n",
            "19\n",
            "4\n",
            "49\n",
            "2\n",
            "5\n",
            "31\n",
            "19\n",
            "4\n",
            "1\n",
            "7\n",
            "GOOD Q (left to the green): 0.8658484124626393 , BAD Q (under the red) 0.15868241584542492 , BAD Q 2(left of the red) 0.22099136947340375\n",
            "GOOD V (left to the green): 0.8658484124626393 , BAD V (under the red) 0.22099136947340375\n",
            "MSE_value= tensor(0.1008, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.09,0.93)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.97)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.44,4.02)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.42,-1.99)\n",
            "\n",
            "────────────╭●─────────Rot(0.11,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.42,1.86,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.04,6.59,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(2.01,-5.41,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1008, dtype=torch.float64)\n",
            "GAME = 1980, LOSSavg20 = 0.06665239, number of steps done in last= 7, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = 0.0075\n",
            "37\n",
            "6\n",
            "65\n",
            "14\n",
            "16\n",
            "107\n",
            "46\n",
            "2\n",
            "2\n",
            "31\n",
            "2\n",
            "4\n",
            "112\n",
            "31\n",
            "3\n",
            "16\n",
            "43\n",
            "1\n",
            "19\n",
            "53\n",
            "GOOD Q (left to the green): 0.8876955227405214 , BAD Q (under the red) 0.1646173299845068 , BAD Q 2(left of the red) 0.26294173181812286\n",
            "GOOD V (left to the green): 0.8876955227405214 , BAD V (under the red) 0.26294173181812286\n",
            "MSE_value= tensor(0.1079, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.07,0.93)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-1.97)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.41,4.00)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.45,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(0.11,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.42,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.05,6.59,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.96,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1079, dtype=torch.float64)\n",
            "GAME = 2000, LOSSavg20 = 0.06681665, number of steps done in last= 53, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = 0.0059\n",
            "1\n",
            "1\n",
            "21\n",
            "9\n",
            "10\n",
            "5\n",
            "12\n",
            "5\n",
            "34\n",
            "2\n",
            "7\n",
            "12\n",
            "7\n",
            "28\n",
            "31\n",
            "13\n",
            "43\n",
            "2\n",
            "3\n",
            "17\n",
            "GOOD Q (left to the green): 0.862150529007272 , BAD Q (under the red) 0.1629484594770645 , BAD Q 2(left of the red) 0.2508072234092949\n",
            "GOOD V (left to the green): 0.862150529007272 , BAD V (under the red) 0.2508072234092949\n",
            "MSE_value= tensor(0.1180, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.08,0.93)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-1.97)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.42,4.01)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.47,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.12,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.09,6.57,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.94,-5.45,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1180, dtype=torch.float64)\n",
            "GAME = 2020, LOSSavg20 = 0.06725047, number of steps done in last= 17, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = 0.0046\n",
            "22\n",
            "8\n",
            "4\n",
            "7\n",
            "10\n",
            "1\n",
            "2\n",
            "57\n",
            "1\n",
            "36\n",
            "3\n",
            "26\n",
            "3\n",
            "1\n",
            "29\n",
            "8\n",
            "3\n",
            "28\n",
            "5\n",
            "6\n",
            "GOOD Q (left to the green): 0.8418001261403256 , BAD Q (under the red) 0.11245188604179335 , BAD Q 2(left of the red) 0.2288691668911998\n",
            "GOOD V (left to the green): 0.8418001261403256 , BAD V (under the red) 0.2288691668911998\n",
            "MSE_value= tensor(0.1272, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.10,0.93)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.98)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.42,4.02)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.47,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.85,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.10,6.58,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.94,-5.45,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1272, dtype=torch.float64)\n",
            "GAME = 2040, LOSSavg20 = 0.06719603, number of steps done in last= 6, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = 0.0034\n",
            "3\n",
            "1\n",
            "7\n",
            "2\n",
            "8\n",
            "9\n",
            "4\n",
            "31\n",
            "22\n",
            "28\n",
            "2\n",
            "1\n",
            "8\n",
            "19\n",
            "11\n",
            "13\n",
            "52\n",
            "24\n",
            "73\n",
            "1\n",
            "GOOD Q (left to the green): 0.8197092128330528 , BAD Q (under the red) 0.03681883355644633 , BAD Q 2(left of the red) 0.19534288086696822\n",
            "GOOD V (left to the green): 0.8197092128330528 , BAD V (under the red) 0.19534288086696822\n",
            "MSE_value= tensor(0.1456, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.92)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.01)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.43,4.04)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.47,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.37,1.87,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.11,6.58,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.94,-5.45,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1456, dtype=torch.float64)\n",
            "GAME = 2060, LOSSavg20 = 0.06744774, number of steps done in last= 1, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0009\n",
            "34\n",
            "9\n",
            "16\n",
            "57\n",
            "40\n",
            "59\n",
            "9\n",
            "3\n",
            "14\n",
            "2\n",
            "5\n",
            "13\n",
            "42\n",
            "24\n",
            "2\n",
            "12\n",
            "12\n",
            "48\n",
            "72\n",
            "45\n",
            "GOOD Q (left to the green): 0.8229731193489356 , BAD Q (under the red) 0.02284905455663938 , BAD Q 2(left of the red) 0.22422967555986373\n",
            "GOOD V (left to the green): 0.8229731193489356 , BAD V (under the red) 0.22422967555986373\n",
            "MSE_value= tensor(0.1369, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.12,0.92)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.01)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.40,4.02)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.46,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.29,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.38,1.85,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.11,6.61,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.45,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1369, dtype=torch.float64)\n",
            "GAME = 2080, LOSSavg20 = 0.06775593, number of steps done in last= 45, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0013\n",
            "16\n",
            "27\n",
            "9\n",
            "103\n",
            "43\n",
            "8\n",
            "36\n",
            "1\n",
            "26\n",
            "69\n",
            "78\n",
            "10\n",
            "1\n",
            "2\n",
            "4\n",
            "12\n",
            "1\n",
            "2\n",
            "14\n",
            "92\n",
            "GOOD Q (left to the green): 0.8306104550951264 , BAD Q (under the red) 0.12956194762000206 , BAD Q 2(left of the red) 0.2912924390001833\n",
            "GOOD V (left to the green): 0.8306104550951264 , BAD V (under the red) 0.2912924390001833\n",
            "MSE_value= tensor(0.1054, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.10,0.95)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-1.98)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.38,3.98)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.45,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.81,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.07,6.63,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.94,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1054, dtype=torch.float64)\n",
            "GAME = 2100, LOSSavg20 = 0.06819253, number of steps done in last= 92, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0019\n",
            "34\n",
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            "3\n",
            "35\n",
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            "18\n",
            "1\n",
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            "16\n",
            "44\n",
            "22\n",
            "1\n",
            "GOOD Q (left to the green): 0.8148193634809838 , BAD Q (under the red) 0.05003046924488597 , BAD Q 2(left of the red) 0.2925125001707964\n",
            "GOOD V (left to the green): 0.8148193634809838 , BAD V (under the red) 0.2925125001707964\n",
            "MSE_value= tensor(0.1197, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.12,0.93)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-1.99)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.40,3.99)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.44,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.11,6.61,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.95,-5.43,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1197, dtype=torch.float64)\n",
            "GAME = 2120, LOSSavg20 = 0.06864854, number of steps done in last= 1, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0029\n",
            "6\n",
            "16\n",
            "1\n",
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            "18\n",
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            "4\n",
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            "14\n",
            "41\n",
            "15\n",
            "2\n",
            "39\n",
            "3\n",
            "GOOD Q (left to the green): 0.7746889076857189 , BAD Q (under the red) -0.08103872839633663 , BAD Q 2(left of the red) 0.24117414180660923\n",
            "GOOD V (left to the green): 0.7746889076857189 , BAD V (under the red) 0.24117414180660923\n",
            "MSE_value= tensor(0.1465, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.15,0.91)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.03)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.38,4.03)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.44,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.05,-1.31,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.38,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.14,6.59,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.96,-5.42,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1465, dtype=torch.float64)\n",
            "GAME = 2140, LOSSavg20 = 0.06946289, number of steps done in last= 3, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0044\n",
            "18\n",
            "4\n",
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            "60\n",
            "15\n",
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            "53\n",
            "8\n",
            "9\n",
            "98\n",
            "4\n",
            "GOOD Q (left to the green): 0.7763307068616185 , BAD Q (under the red) -0.0016770238908963564 , BAD Q 2(left of the red) 0.2963767267734041\n",
            "GOOD V (left to the green): 0.7763307068616185 , BAD V (under the red) 0.2963767267734041\n",
            "MSE_value= tensor(0.1243, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.93)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.39,3.99)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.44,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.30,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.42,1.80,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.18,6.60,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.95,-5.43,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1243, dtype=torch.float64)\n",
            "GAME = 2160, LOSSavg20 = 0.06991042, number of steps done in last= 4, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0042\n",
            "6\n",
            "17\n",
            "18\n",
            "5\n",
            "8\n",
            "8\n",
            "36\n",
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            "14\n",
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            "27\n",
            "15\n",
            "41\n",
            "GOOD Q (left to the green): 0.7848526002254708 , BAD Q (under the red) 0.17343206758961227 , BAD Q 2(left of the red) 0.2661614485387397\n",
            "GOOD V (left to the green): 0.7848526002254708 , BAD V (under the red) 0.2661614485387397\n",
            "MSE_value= tensor(0.1077, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.10,0.98)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.86,-1.96)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.39,4.00)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.44,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.42,1.80,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.24,6.57,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.95,-5.43,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1077, dtype=torch.float64)\n",
            "GAME = 2180, LOSSavg20 = 0.06994217, number of steps done in last= 41, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0051\n",
            "40\n",
            "8\n",
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            "36\n",
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            "3\n",
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            "2\n",
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            "11\n",
            "2\n",
            "58\n",
            "GOOD Q (left to the green): 0.8210587938004481 , BAD Q (under the red) 0.18575806111205406 , BAD Q 2(left of the red) 0.2602015827007048\n",
            "GOOD V (left to the green): 0.8210587938004481 , BAD V (under the red) 0.2602015827007048\n",
            "MSE_value= tensor(0.1061, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.08,0.98)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.86,-1.96)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.36,3.99)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.46,-1.91)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.42,1.81,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.25,6.57,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1061, dtype=torch.float64)\n",
            "GAME = 2200, LOSSavg20 = 0.06991533, number of steps done in last= 58, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0076\n",
            "15\n",
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            "32\n",
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            "2\n",
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            "39\n",
            "1\n",
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            "14\n",
            "28\n",
            "6\n",
            "4\n",
            "19\n",
            "5\n",
            "47\n",
            "GOOD Q (left to the green): 0.78513639532599 , BAD Q (under the red) 0.1272172839982606 , BAD Q 2(left of the red) 0.20102120390774364\n",
            "GOOD V (left to the green): 0.78513639532599 , BAD V (under the red) 0.20102120390774364\n",
            "MSE_value= tensor(0.1250, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.11,0.98)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.84,-1.99)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,4.02)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.45,-1.91)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.39,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.27,6.57,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.43,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1250, dtype=torch.float64)\n",
            "GAME = 2220, LOSSavg20 = 0.07022734, number of steps done in last= 47, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0090\n",
            "8\n",
            "10\n",
            "3\n",
            "2\n",
            "66\n",
            "4\n",
            "1\n",
            "1\n",
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            "7\n",
            "32\n",
            "1\n",
            "63\n",
            "36\n",
            "3\n",
            "42\n",
            "42\n",
            "14\n",
            "5\n",
            "19\n",
            "GOOD Q (left to the green): 0.7570679299823244 , BAD Q (under the red) -0.030993395584204497 , BAD Q 2(left of the red) 0.27523053539304393\n",
            "GOOD V (left to the green): 0.7570679299823244 , BAD V (under the red) 0.27523053539304393\n",
            "MSE_value= tensor(0.1415, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.14,0.94)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,4.00)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.46,-1.91)\n",
            "\n",
            "────────────╭●─────────Rot(0.05,-1.31,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.81,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.28,6.57,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.92,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1415, dtype=torch.float64)\n",
            "GAME = 2240, LOSSavg20 = 0.07044286, number of steps done in last= 19, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0100\n",
            "2\n",
            "2\n",
            "34\n",
            "2\n",
            "22\n",
            "17\n",
            "21\n",
            "7\n",
            "11\n",
            "22\n",
            "10\n",
            "28\n",
            "9\n",
            "8\n",
            "30\n",
            "58\n",
            "46\n",
            "22\n",
            "19\n",
            "6\n",
            "GOOD Q (left to the green): 0.782777520036831 , BAD Q (under the red) -0.016850084080963335 , BAD Q 2(left of the red) 0.2775702802229071\n",
            "GOOD V (left to the green): 0.782777520036831 , BAD V (under the red) 0.2775702802229071\n",
            "MSE_value= tensor(0.1472, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.95)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.38,3.99)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.50,-1.87)\n",
            "\n",
            "────────────╭●─────────Rot(0.05,-1.30,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.31,6.56,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.49,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1472, dtype=torch.float64)\n",
            "GAME = 2260, LOSSavg20 = 0.07085819, number of steps done in last= 6, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0123\n",
            "42\n",
            "29\n",
            "23\n",
            "2\n",
            "9\n",
            "4\n",
            "20\n",
            "5\n",
            "34\n",
            "20\n",
            "92\n",
            "36\n",
            "34\n",
            "11\n",
            "77\n",
            "18\n",
            "1\n",
            "5\n",
            "25\n",
            "1\n",
            "GOOD Q (left to the green): 0.7942270853071925 , BAD Q (under the red) 0.05579124730979074 , BAD Q 2(left of the red) 0.21009721261481284\n",
            "GOOD V (left to the green): 0.7942270853071925 , BAD V (under the red) 0.21009721261481284\n",
            "MSE_value= tensor(0.1457, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.97)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.86,-2.03)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,4.02)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.51,-1.87)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.38,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.33,6.54,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.49,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1457, dtype=torch.float64)\n",
            "GAME = 2280, LOSSavg20 = 0.07120758, number of steps done in last= 1, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0133\n",
            "3\n",
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            "42\n",
            "1\n",
            "GOOD Q (left to the green): 0.7760566676977024 , BAD Q (under the red) 0.0408299946905763 , BAD Q 2(left of the red) 0.2084700398632292\n",
            "GOOD V (left to the green): 0.7760566676977024 , BAD V (under the red) 0.2084700398632292\n",
            "MSE_value= tensor(0.1529, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.15,0.98)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.86,-2.03)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.40,4.02)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.50,-1.88)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.37,1.85,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.36,6.55,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.48,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1529, dtype=torch.float64)\n",
            "GAME = 2300, LOSSavg20 = 0.07120576, number of steps done in last= 1, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0125\n",
            "3\n",
            "45\n",
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            "11\n",
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            "7\n",
            "82\n",
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            "1\n",
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            "24\n",
            "6\n",
            "23\n",
            "11\n",
            "42\n",
            "16\n",
            "76\n",
            "GOOD Q (left to the green): 0.7870255012897724 , BAD Q (under the red) -0.005344186976882048 , BAD Q 2(left of the red) 0.2485149433073648\n",
            "GOOD V (left to the green): 0.7870255012897724 , BAD V (under the red) 0.2485149433073648\n",
            "MSE_value= tensor(0.1531, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.15,0.97)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.03)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.41,3.99)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.51,-1.89)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.29,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.39,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.34,6.60,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.50,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1531, dtype=torch.float64)\n",
            "GAME = 2320, LOSSavg20 = 0.07135201, number of steps done in last= 76, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0148\n",
            "7\n",
            "42\n",
            "8\n",
            "1\n",
            "7\n",
            "11\n",
            "4\n",
            "64\n",
            "44\n",
            "10\n",
            "1\n",
            "1\n",
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            "21\n",
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            "11\n",
            "13\n",
            "26\n",
            "35\n",
            "8\n",
            "GOOD Q (left to the green): 0.7977064711468481 , BAD Q (under the red) 0.04985380030459785 , BAD Q 2(left of the red) 0.2668678263120867\n",
            "GOOD V (left to the green): 0.7977064711468481 , BAD V (under the red) 0.2668678263120867\n",
            "MSE_value= tensor(0.1383, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.14,0.98)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.40,3.97)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.50,-1.89)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.34,6.61,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.50,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1383, dtype=torch.float64)\n",
            "GAME = 2340, LOSSavg20 = 0.07144227, number of steps done in last= 8, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0144\n",
            "8\n",
            "1\n",
            "29\n",
            "21\n",
            "13\n",
            "15\n",
            "44\n",
            "39\n",
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            "3\n",
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            "95\n",
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            "32\n",
            "21\n",
            "114\n",
            "12\n",
            "20\n",
            "37\n",
            "22\n",
            "GOOD Q (left to the green): 0.7870827089022 , BAD Q (under the red) 0.10800878354177684 , BAD Q 2(left of the red) 0.30807022479977547\n",
            "GOOD V (left to the green): 0.7870827089022 , BAD V (under the red) 0.30807022479977547\n",
            "MSE_value= tensor(0.1233, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.12,0.99)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-1.99)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.40,3.96)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.48,-1.90)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.48,1.80,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.37,6.58,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.90,-5.47,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1233, dtype=torch.float64)\n",
            "GAME = 2360, LOSSavg20 = 0.07151909, number of steps done in last= 22, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0132\n",
            "1\n",
            "25\n",
            "1\n",
            "35\n",
            "10\n",
            "120\n",
            "34\n",
            "8\n",
            "3\n",
            "58\n",
            "38\n",
            "1\n",
            "7\n",
            "7\n",
            "21\n",
            "8\n",
            "3\n",
            "2\n",
            "20\n",
            "26\n",
            "GOOD Q (left to the green): 0.7908122907904171 , BAD Q (under the red) 0.03609667096693797 , BAD Q 2(left of the red) 0.3220125217960872\n",
            "GOOD V (left to the green): 0.7908122907904171 , BAD V (under the red) 0.3220125217960872\n",
            "MSE_value= tensor(0.1125, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.98)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.39,3.93)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.47,-1.92)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.29,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.52,1.80,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.34,6.61,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.91,-5.46,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1125, dtype=torch.float64)\n",
            "GAME = 2380, LOSSavg20 = 0.07148508, number of steps done in last= 26, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0132\n",
            "26\n",
            "20\n",
            "3\n",
            "52\n",
            "20\n",
            "3\n",
            "18\n",
            "4\n",
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            "19\n",
            "12\n",
            "33\n",
            "1\n",
            "60\n",
            "7\n",
            "2\n",
            "10\n",
            "2\n",
            "21\n",
            "7\n",
            "GOOD Q (left to the green): 0.8252509757517787 , BAD Q (under the red) 0.06883739007333473 , BAD Q 2(left of the red) 0.3043263976585547\n",
            "GOOD V (left to the green): 0.8252509757517787 , BAD V (under the red) 0.3043263976585547\n",
            "MSE_value= tensor(0.1108, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.12,0.99)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.00)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,3.92)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.46,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.50,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.32,6.63,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.91,-5.46,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1108, dtype=torch.float64)\n",
            "GAME = 2400, LOSSavg20 = 0.07157237, number of steps done in last= 7, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0133\n",
            "33\n",
            "100\n",
            "62\n",
            "8\n",
            "3\n",
            "22\n",
            "9\n",
            "1\n",
            "9\n",
            "15\n",
            "2\n",
            "1\n",
            "2\n",
            "18\n",
            "38\n",
            "14\n",
            "23\n",
            "6\n",
            "2\n",
            "24\n",
            "GOOD Q (left to the green): 0.8352830152449151 , BAD Q (under the red) 0.10836151447647119 , BAD Q 2(left of the red) 0.2736697088551697\n",
            "GOOD V (left to the green): 0.8352830152449151 , BAD V (under the red) 0.2736697088551697\n",
            "MSE_value= tensor(0.1050, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.12,1.00)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.02)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,3.92)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.44,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.47,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.30,6.67,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.92,-5.45,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1050, dtype=torch.float64)\n",
            "GAME = 2420, LOSSavg20 = 0.07196102, number of steps done in last= 24, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0159\n",
            "5\n",
            "5\n",
            "67\n",
            "1\n",
            "3\n",
            "32\n",
            "19\n",
            "13\n",
            "5\n",
            "1\n",
            "38\n",
            "3\n",
            "5\n",
            "6\n",
            "5\n",
            "12\n",
            "3\n",
            "37\n",
            "17\n",
            "69\n",
            "GOOD Q (left to the green): 0.8165291276568964 , BAD Q (under the red) 0.08874701637952354 , BAD Q 2(left of the red) 0.25187110889886294\n",
            "GOOD V (left to the green): 0.8165291276568964 , BAD V (under the red) 0.25187110889886294\n",
            "MSE_value= tensor(0.1175, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,1.00)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.03)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,3.94)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.46,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.45,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.31,6.67,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.47,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1175, dtype=torch.float64)\n",
            "GAME = 2440, LOSSavg20 = 0.07236169, number of steps done in last= 69, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0183\n",
            "12\n",
            "2\n",
            "19\n",
            "2\n",
            "34\n",
            "76\n",
            "37\n",
            "57\n",
            "26\n",
            "12\n",
            "78\n",
            "32\n",
            "37\n",
            "31\n",
            "15\n",
            "10\n",
            "25\n",
            "37\n",
            "28\n",
            "7\n",
            "GOOD Q (left to the green): 0.8262078211057122 , BAD Q (under the red) 0.044802614919149775 , BAD Q 2(left of the red) 0.30946924690737826\n",
            "GOOD V (left to the green): 0.8262078211057122 , BAD V (under the red) 0.30946924690737826\n",
            "MSE_value= tensor(0.1167, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.99)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.04)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.36,3.91)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.47,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.46,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.32,6.66,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.48,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1167, dtype=torch.float64)\n",
            "GAME = 2460, LOSSavg20 = 0.07262348, number of steps done in last= 7, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0194\n",
            "58\n",
            "4\n",
            "16\n",
            "2\n",
            "24\n",
            "4\n",
            "3\n",
            "11\n",
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            "10\n",
            "44\n",
            "5\n",
            "38\n",
            "5\n",
            "24\n",
            "2\n",
            "20\n",
            "2\n",
            "19\n",
            "44\n",
            "GOOD Q (left to the green): 0.8113304532790773 , BAD Q (under the red) 0.02213047757579001 , BAD Q 2(left of the red) 0.3323975601538509\n",
            "GOOD V (left to the green): 0.8113304532790773 , BAD V (under the red) 0.3323975601538509\n",
            "MSE_value= tensor(0.1153, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.99)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.03)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.35,3.90)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.49,-1.90)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.29,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.48,1.80,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.34,6.65,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.86,-5.50,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1153, dtype=torch.float64)\n",
            "GAME = 2480, LOSSavg20 = 0.07276634, number of steps done in last= 44, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0204\n",
            "33\n",
            "21\n",
            "4\n",
            "29\n",
            "25\n",
            "1\n",
            "70\n",
            "10\n",
            "5\n",
            "12\n",
            "28\n",
            "2\n",
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            "3\n",
            "31\n",
            "3\n",
            "2\n",
            "12\n",
            "18\n",
            "7\n",
            "GOOD Q (left to the green): 0.8023448440724068 , BAD Q (under the red) 0.033614665013162126 , BAD Q 2(left of the red) 0.30175888441427035\n",
            "GOOD V (left to the green): 0.8023448440724068 , BAD V (under the red) 0.30175888441427035\n",
            "MSE_value= tensor(0.1256, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,0.99)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.04)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.32,3.93)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.51,-1.87)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.29,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.47,1.81,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.38,6.61,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.85,-5.51,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1256, dtype=torch.float64)\n",
            "GAME = 2500, LOSSavg20 = 0.07299451, number of steps done in last= 7, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0204\n",
            "1\n",
            "6\n",
            "106\n",
            "5\n",
            "38\n",
            "29\n",
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            "26\n",
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            "12\n",
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            "3\n",
            "19\n",
            "3\n",
            "1\n",
            "26\n",
            "19\n",
            "17\n",
            "GOOD Q (left to the green): 0.8258979939538954 , BAD Q (under the red) 0.10403959726628675 , BAD Q 2(left of the red) 0.2859933495631466\n",
            "GOOD V (left to the green): 0.8258979939538954 , BAD V (under the red) 0.2859933495631466\n",
            "MSE_value= tensor(0.1184, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.11,1.01)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.04)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.32,3.94)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.49,-1.87)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.44,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.41,6.57,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.86,-5.49,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1184, dtype=torch.float64)\n",
            "GAME = 2520, LOSSavg20 = 0.07339472, number of steps done in last= 17, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0227\n",
            "23\n",
            "62\n",
            "33\n",
            "29\n",
            "2\n",
            "17\n",
            "7\n",
            "35\n",
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            "20\n",
            "36\n",
            "2\n",
            "49\n",
            "16\n",
            "45\n",
            "1\n",
            "5\n",
            "5\n",
            "32\n",
            "GOOD Q (left to the green): 0.8446613514224928 , BAD Q (under the red) 0.1940287195241065 , BAD Q 2(left of the red) 0.2624687963733532\n",
            "GOOD V (left to the green): 0.8446613514224928 , BAD V (under the red) 0.2624687963733532\n",
            "MSE_value= tensor(0.1023, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.11,1.06)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.86,-2.04)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.34,3.94)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.48,-1.88)\n",
            "\n",
            "────────────╭●─────────Rot(0.12,-1.24,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.42,6.58,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.87,-5.48,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1023, dtype=torch.float64)\n",
            "GAME = 2540, LOSSavg20 = 0.07393320, number of steps done in last= 32, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0237\n",
            "14\n",
            "6\n",
            "20\n",
            "1\n",
            "32\n",
            "4\n",
            "5\n",
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            "6\n",
            "16\n",
            "10\n",
            "45\n",
            "36\n",
            "3\n",
            "GOOD Q (left to the green): 0.8494081103209202 , BAD Q (under the red) 0.14257700107312665 , BAD Q 2(left of the red) 0.28014634295103297\n",
            "GOOD V (left to the green): 0.8494081103209202 , BAD V (under the red) 0.28014634295103297\n",
            "MSE_value= tensor(0.1142, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.12,1.05)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.05)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.34,3.93)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.48,-1.88)\n",
            "\n",
            "────────────╭●─────────Rot(0.11,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.43,6.59,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.86,-5.48,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1142, dtype=torch.float64)\n",
            "GAME = 2560, LOSSavg20 = 0.07421488, number of steps done in last= 3, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0256\n",
            "45\n",
            "51\n",
            "1\n",
            "76\n",
            "89\n",
            "37\n",
            "5\n",
            "93\n",
            "5\n",
            "8\n",
            "16\n",
            "64\n",
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            "29\n",
            "8\n",
            "1\n",
            "16\n",
            "37\n",
            "6\n",
            "11\n",
            "GOOD Q (left to the green): 0.8509183976032537 , BAD Q (under the red) 0.03629257139649483 , BAD Q 2(left of the red) 0.3270067726840357\n",
            "GOOD V (left to the green): 0.8509183976032537 , BAD V (under the red) 0.3270067726840357\n",
            "MSE_value= tensor(0.1272, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.14,1.02)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.08)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.34,3.91)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.47,-1.88)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.44,6.59,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.86,-5.48,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1272, dtype=torch.float64)\n",
            "GAME = 2580, LOSSavg20 = 0.07440193, number of steps done in last= 11, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0273\n",
            "2\n",
            "35\n",
            "1\n",
            "5\n",
            "81\n",
            "37\n",
            "7\n",
            "1\n",
            "30\n",
            "1\n",
            "107\n",
            "2\n",
            "5\n",
            "18\n",
            "4\n",
            "3\n",
            "1\n",
            "3\n",
            "9\n",
            "25\n",
            "GOOD Q (left to the green): 0.8303832288382411 , BAD Q (under the red) 0.08160866797664346 , BAD Q 2(left of the red) 0.30658460118827663\n",
            "GOOD V (left to the green): 0.8303832288382411 , BAD V (under the red) 0.30658460118827663\n",
            "MSE_value= tensor(0.1257, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.15,1.04)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.07)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,3.93)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.47,-1.89)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.48,6.60,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.87,-5.47,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1257, dtype=torch.float64)\n",
            "GAME = 2600, LOSSavg20 = 0.07444493, number of steps done in last= 25, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0280\n",
            "16\n",
            "1\n",
            "137\n",
            "7\n",
            "15\n",
            "15\n",
            "47\n",
            "11\n",
            "17\n",
            "2\n",
            "25\n",
            "25\n",
            "39\n",
            "15\n",
            "74\n",
            "39\n",
            "4\n",
            "2\n",
            "1\n",
            "48\n",
            "GOOD Q (left to the green): 0.8016551659543687 , BAD Q (under the red) 0.022852121956449534 , BAD Q 2(left of the red) 0.2904030721506955\n",
            "GOOD V (left to the green): 0.8016551659543687 , BAD V (under the red) 0.2904030721506955\n",
            "MSE_value= tensor(0.1394, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.16,1.03)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.08)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,3.94)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.48,-1.88)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.50,6.60,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.86,-5.49,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1394, dtype=torch.float64)\n",
            "GAME = 2620, LOSSavg20 = 0.07450348, number of steps done in last= 48, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0297\n",
            "25\n",
            "57\n",
            "6\n",
            "59\n",
            "56\n",
            "6\n",
            "31\n",
            "4\n",
            "37\n",
            "1\n",
            "27\n",
            "125\n",
            "20\n",
            "12\n",
            "9\n",
            "4\n",
            "15\n",
            "3\n",
            "65\n",
            "10\n",
            "GOOD Q (left to the green): 0.7572279647594943 , BAD Q (under the red) 0.024371540778186984 , BAD Q 2(left of the red) 0.26005266329722593\n",
            "GOOD V (left to the green): 0.7572279647594943 , BAD V (under the red) 0.26005266329722593\n",
            "MSE_value= tensor(0.1514, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.18,1.04)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.08)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,3.96)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.51,-1.86)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.52,6.58,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.84,-5.51,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1514, dtype=torch.float64)\n",
            "GAME = 2640, LOSSavg20 = 0.07463869, number of steps done in last= 10, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0307\n",
            "1\n",
            "28\n",
            "2\n",
            "1\n",
            "31\n",
            "1\n",
            "29\n",
            "1\n",
            "51\n",
            "8\n",
            "8\n",
            "24\n",
            "6\n",
            "1\n",
            "2\n",
            "2\n",
            "12\n",
            "2\n",
            "12\n",
            "4\n",
            "GOOD Q (left to the green): 0.7415172106593744 , BAD Q (under the red) -0.011196657283184086 , BAD Q 2(left of the red) 0.2274248056097472\n",
            "GOOD V (left to the green): 0.7415172106593744 , BAD V (under the red) 0.2274248056097472\n",
            "MSE_value= tensor(0.1560, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.19,1.04)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.87,-2.09)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,3.97)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.47,-1.90)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.39,1.85,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.52,6.58,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.47,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1560, dtype=torch.float64)\n",
            "GAME = 2660, LOSSavg20 = 0.07465606, number of steps done in last= 4, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0325\n",
            "1\n",
            "1\n",
            "2\n",
            "110\n",
            "24\n",
            "4\n",
            "24\n",
            "10\n",
            "22\n",
            "2\n",
            "5\n",
            "12\n",
            "4\n",
            "6\n",
            "3\n",
            "5\n",
            "49\n",
            "60\n",
            "23\n",
            "10\n",
            "GOOD Q (left to the green): 0.7321729302334354 , BAD Q (under the red) -0.016671585793795574 , BAD Q 2(left of the red) 0.2257508274279451\n",
            "GOOD V (left to the green): 0.7321729302334354 , BAD V (under the red) 0.2257508274279451\n",
            "MSE_value= tensor(0.1498, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.20,1.05)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.86,-2.09)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.36,3.97)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.43,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.29,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.39,1.85,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.52,6.59,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.91,-5.43,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1498, dtype=torch.float64)\n",
            "GAME = 2680, LOSSavg20 = 0.07505995, number of steps done in last= 10, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0356\n",
            "10\n",
            "5\n",
            "6\n",
            "4\n",
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            "37\n",
            "19\n",
            "65\n",
            "7\n",
            "16\n",
            "7\n",
            "1\n",
            "6\n",
            "28\n",
            "19\n",
            "1\n",
            "2\n",
            "14\n",
            "3\n",
            "60\n",
            "GOOD Q (left to the green): 0.7873709142681875 , BAD Q (under the red) 0.04673527922081655 , BAD Q 2(left of the red) 0.2793165414882144\n",
            "GOOD V (left to the green): 0.7873709142681875 , BAD V (under the red) 0.2793165414882144\n",
            "MSE_value= tensor(0.1301, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.16,1.06)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.07)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.34,3.93)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.45,-1.90)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.52,6.59,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.46,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1301, dtype=torch.float64)\n",
            "GAME = 2700, LOSSavg20 = 0.07515450, number of steps done in last= 60, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0360\n",
            "6\n",
            "2\n",
            "6\n",
            "20\n",
            "53\n",
            "43\n",
            "12\n",
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            "1\n",
            "2\n",
            "19\n",
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            "4\n",
            "4\n",
            "56\n",
            "4\n",
            "14\n",
            "19\n",
            "43\n",
            "3\n",
            "GOOD Q (left to the green): 0.7819786441893601 , BAD Q (under the red) -0.022473986771948395 , BAD Q 2(left of the red) 0.2620807933450818\n",
            "GOOD V (left to the green): 0.7819786441893601 , BAD V (under the red) 0.2620807933450818\n",
            "MSE_value= tensor(0.1451, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.18,1.05)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.10)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.36,3.95)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.44,-1.91)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.29,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.38,1.85,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.55,6.58,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1451, dtype=torch.float64)\n",
            "GAME = 2720, LOSSavg20 = 0.07520661, number of steps done in last= 3, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0376\n",
            "6\n",
            "12\n",
            "1\n",
            "7\n",
            "2\n",
            "10\n",
            "2\n",
            "7\n",
            "26\n",
            "85\n",
            "2\n",
            "1\n",
            "15\n",
            "47\n",
            "4\n",
            "2\n",
            "57\n",
            "32\n",
            "25\n",
            "17\n",
            "GOOD Q (left to the green): 0.7805335168535332 , BAD Q (under the red) -0.007451261339458726 , BAD Q 2(left of the red) 0.26757707019783616\n",
            "GOOD V (left to the green): 0.7805335168535332 , BAD V (under the red) 0.26757707019783616\n",
            "MSE_value= tensor(0.1387, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.18,1.05)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.10)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,3.95)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.42,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.38,1.85,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.57,6.57,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.91,-5.42,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1387, dtype=torch.float64)\n",
            "GAME = 2740, LOSSavg20 = 0.07528092, number of steps done in last= 17, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0389\n",
            "18\n",
            "5\n",
            "7\n",
            "3\n",
            "2\n",
            "84\n",
            "35\n",
            "29\n",
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            "8\n",
            "17\n",
            "7\n",
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            "23\n",
            "21\n",
            "21\n",
            "19\n",
            "8\n",
            "4\n",
            "25\n",
            "GOOD Q (left to the green): 0.7642501604066798 , BAD Q (under the red) -0.09542735480693076 , BAD Q 2(left of the red) 0.27001351035985943\n",
            "GOOD V (left to the green): 0.7642501604066798 , BAD V (under the red) 0.27001351035985943\n",
            "MSE_value= tensor(0.1544, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.20,1.03)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.12)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,3.96)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.40,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(0.05,-1.30,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.38,1.85,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.61,6.58,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.92,-5.41,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1544, dtype=torch.float64)\n",
            "GAME = 2760, LOSSavg20 = 0.07532767, number of steps done in last= 25, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0413\n",
            "4\n",
            "17\n",
            "9\n",
            "1\n",
            "16\n",
            "12\n",
            "37\n",
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            "34\n",
            "11\n",
            "1\n",
            "21\n",
            "35\n",
            "19\n",
            "5\n",
            "139\n",
            "27\n",
            "11\n",
            "2\n",
            "GOOD Q (left to the green): 0.7956642095350772 , BAD Q (under the red) -0.045255867902382174 , BAD Q 2(left of the red) 0.2842946697576785\n",
            "GOOD V (left to the green): 0.7956642095350772 , BAD V (under the red) 0.2842946697576785\n",
            "MSE_value= tensor(0.1428, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.17,1.04)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.35,3.94)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.43,-1.92)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.30,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.39,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.62,6.58,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.90,-5.43,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1428, dtype=torch.float64)\n",
            "GAME = 2780, LOSSavg20 = 0.07555135, number of steps done in last= 2, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0417\n",
            "25\n",
            "20\n",
            "8\n",
            "64\n",
            "7\n",
            "25\n",
            "1\n",
            "30\n",
            "76\n",
            "15\n",
            "14\n",
            "59\n",
            "4\n",
            "12\n",
            "25\n",
            "22\n",
            "13\n",
            "21\n",
            "31\n",
            "6\n",
            "GOOD Q (left to the green): 0.7736921223130373 , BAD Q (under the red) -0.028569280749055515 , BAD Q 2(left of the red) 0.2940627637606327\n",
            "GOOD V (left to the green): 0.7736921223130373 , BAD V (under the red) 0.2940627637606327\n",
            "MSE_value= tensor(0.1413, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.17,1.04)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.10)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.35,3.95)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.43,-1.92)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.29,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.42,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.65,6.57,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.90,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1413, dtype=torch.float64)\n",
            "GAME = 2800, LOSSavg20 = 0.07574667, number of steps done in last= 6, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0426\n",
            "28\n",
            "31\n",
            "23\n",
            "20\n",
            "16\n",
            "4\n",
            "20\n",
            "15\n",
            "40\n",
            "1\n",
            "30\n",
            "30\n",
            "33\n",
            "18\n",
            "22\n",
            "15\n",
            "33\n",
            "67\n",
            "8\n",
            "39\n",
            "GOOD Q (left to the green): 0.8271018722455388 , BAD Q (under the red) 0.09388219395132147 , BAD Q 2(left of the red) 0.2386434670099376\n",
            "GOOD V (left to the green): 0.8271018722455388 , BAD V (under the red) 0.2386434670099376\n",
            "MSE_value= tensor(0.1155, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.14,1.08)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.85,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.31,3.94)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.43,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.38,1.85,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.61,6.61,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1155, dtype=torch.float64)\n",
            "GAME = 2820, LOSSavg20 = 0.07588286, number of steps done in last= 39, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0432\n",
            "32\n",
            "13\n",
            "50\n",
            "1\n",
            "1\n",
            "12\n",
            "1\n",
            "21\n",
            "14\n",
            "31\n",
            "5\n",
            "76\n",
            "46\n",
            "14\n",
            "5\n",
            "34\n",
            "3\n",
            "5\n",
            "11\n",
            "13\n",
            "GOOD Q (left to the green): 0.8298746286125952 , BAD Q (under the red) 0.11607278761002016 , BAD Q 2(left of the red) 0.28729130919942114\n",
            "GOOD V (left to the green): 0.8298746286125952 , BAD V (under the red) 0.28729130919942114\n",
            "MSE_value= tensor(0.0952, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.14,1.09)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.86,-2.10)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.31,3.90)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.39,-1.98)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.39,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.56,6.65,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.41,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0952, dtype=torch.float64)\n",
            "GAME = 2840, LOSSavg20 = 0.07605662, number of steps done in last= 13, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0432\n",
            "37\n",
            "7\n",
            "2\n",
            "4\n",
            "3\n",
            "1\n",
            "30\n",
            "12\n",
            "13\n",
            "7\n",
            "16\n",
            "1\n",
            "3\n",
            "1\n",
            "23\n",
            "5\n",
            "52\n",
            "5\n",
            "16\n",
            "28\n",
            "GOOD Q (left to the green): 0.8145000701240431 , BAD Q (under the red) 0.11117254839649021 , BAD Q 2(left of the red) 0.2700058058124174\n",
            "GOOD V (left to the green): 0.8145000701240431 , BAD V (under the red) 0.2700058058124174\n",
            "MSE_value= tensor(0.0974, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.15,1.09)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.86,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.32,3.92)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.38,-1.98)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.37,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.57,6.65,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.40,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0974, dtype=torch.float64)\n",
            "GAME = 2860, LOSSavg20 = 0.07643067, number of steps done in last= 28, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0441\n",
            "3\n",
            "45\n",
            "5\n",
            "40\n",
            "3\n",
            "8\n",
            "1\n",
            "8\n",
            "3\n",
            "67\n",
            "1\n",
            "12\n",
            "19\n",
            "3\n",
            "4\n",
            "9\n",
            "40\n",
            "1\n",
            "45\n",
            "2\n",
            "GOOD Q (left to the green): 0.8300031992649106 , BAD Q (under the red) 0.1143721451126194 , BAD Q 2(left of the red) 0.27796598232274744\n",
            "GOOD V (left to the green): 0.8300031992649106 , BAD V (under the red) 0.27796598232274744\n",
            "MSE_value= tensor(0.0978, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.15,1.10)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.86,-2.12)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.32,3.91)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.38,-1.98)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.36,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.59,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.40,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0978, dtype=torch.float64)\n",
            "GAME = 2880, LOSSavg20 = 0.07662175, number of steps done in last= 2, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0439\n",
            "4\n",
            "55\n",
            "5\n",
            "44\n",
            "10\n",
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            "2\n",
            "34\n",
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            "37\n",
            "16\n",
            "14\n",
            "21\n",
            "43\n",
            "2\n",
            "9\n",
            "4\n",
            "34\n",
            "GOOD Q (left to the green): 0.7995979891141397 , BAD Q (under the red) 0.012282912342517827 , BAD Q 2(left of the red) 0.29984962239679774\n",
            "GOOD V (left to the green): 0.7995979891141397 , BAD V (under the red) 0.29984962239679774\n",
            "MSE_value= tensor(0.1254, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.18,1.08)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.14)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.34,3.93)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.42,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.34,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.63,6.62,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.90,-5.43,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1254, dtype=torch.float64)\n",
            "GAME = 2900, LOSSavg20 = 0.07723333, number of steps done in last= 34, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0452\n",
            "49\n",
            "42\n",
            "33\n",
            "6\n",
            "97\n",
            "10\n",
            "13\n",
            "39\n",
            "17\n",
            "14\n",
            "9\n",
            "10\n",
            "5\n",
            "8\n",
            "1\n",
            "11\n",
            "3\n",
            "6\n",
            "13\n",
            "1\n",
            "GOOD Q (left to the green): 0.8101017648412155 , BAD Q (under the red) 0.11003847383844118 , BAD Q 2(left of the red) 0.3088674807074192\n",
            "GOOD V (left to the green): 0.8101017648412155 , BAD V (under the red) 0.3088674807074192\n",
            "MSE_value= tensor(0.1124, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.16,1.11)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.36,3.92)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.43,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.35,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.65,6.62,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.45,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1124, dtype=torch.float64)\n",
            "GAME = 2920, LOSSavg20 = 0.07771968, number of steps done in last= 1, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0466\n",
            "13\n",
            "8\n",
            "16\n",
            "28\n",
            "8\n",
            "13\n",
            "10\n",
            "11\n",
            "25\n",
            "2\n",
            "1\n",
            "6\n",
            "28\n",
            "9\n",
            "1\n",
            "47\n",
            "13\n",
            "73\n",
            "16\n",
            "22\n",
            "GOOD Q (left to the green): 0.8115004972980033 , BAD Q (under the red) 0.025259545773241743 , BAD Q 2(left of the red) 0.32921405117661995\n",
            "GOOD V (left to the green): 0.8115004972980033 , BAD V (under the red) 0.32921405117661995\n",
            "MSE_value= tensor(0.1189, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.17,1.08)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.12)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.32,3.91)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.43,-1.93)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.37,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.64,6.63,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.45,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1189, dtype=torch.float64)\n",
            "GAME = 2940, LOSSavg20 = 0.07782012, number of steps done in last= 22, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0463\n",
            "10\n",
            "12\n",
            "30\n",
            "11\n",
            "3\n",
            "28\n",
            "8\n",
            "17\n",
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            "52\n",
            "8\n",
            "7\n",
            "96\n",
            "120\n",
            "17\n",
            "37\n",
            "75\n",
            "108\n",
            "5\n",
            "GOOD Q (left to the green): 0.8289757305713887 , BAD Q (under the red) 0.13784056237668013 , BAD Q 2(left of the red) 0.32197759295738965\n",
            "GOOD V (left to the green): 0.8289757305713887 , BAD V (under the red) 0.32197759295738965\n",
            "MSE_value= tensor(0.0905, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.14,1.11)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.87,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.30,3.91)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.37,-1.98)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.37,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.66,6.63,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.39,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0905, dtype=torch.float64)\n",
            "GAME = 2960, LOSSavg20 = 0.07794719, number of steps done in last= 5, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0471\n",
            "58\n",
            "47\n",
            "1\n",
            "4\n",
            "2\n",
            "4\n",
            "4\n",
            "22\n",
            "1\n",
            "50\n",
            "7\n",
            "1\n",
            "1\n",
            "18\n",
            "2\n",
            "60\n",
            "64\n",
            "15\n",
            "25\n",
            "35\n",
            "GOOD Q (left to the green): 0.8640987913775604 , BAD Q (under the red) 0.22800312138263323 , BAD Q 2(left of the red) 0.419895377041943\n",
            "GOOD V (left to the green): 0.8640987913775604 , BAD V (under the red) 0.419895377041943\n",
            "MSE_value= tensor(0.0684, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.10,1.13)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.06)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.29,3.86)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.37,-1.97)\n",
            "\n",
            "────────────╭●─────────Rot(0.12,-1.24,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.77,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.64,6.65,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.91,-5.39,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0684, dtype=torch.float64)\n",
            "GAME = 2980, LOSSavg20 = 0.07822118, number of steps done in last= 35, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0467\n",
            "31\n",
            "31\n",
            "25\n",
            "4\n",
            "32\n",
            "5\n",
            "33\n",
            "9\n",
            "17\n",
            "18\n",
            "12\n",
            "12\n",
            "45\n",
            "37\n",
            "1\n",
            "4\n",
            "1\n",
            "12\n",
            "17\n",
            "17\n",
            "GOOD Q (left to the green): 0.8260649579263741 , BAD Q (under the red) 0.10934265443812535 , BAD Q 2(left of the red) 0.39764520501869915\n",
            "GOOD V (left to the green): 0.8260649579263741 , BAD V (under the red) 0.39764520501869915\n",
            "MSE_value= tensor(0.0946, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.14,1.10)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.09)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.29,3.88)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.39,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.78,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.66,6.63,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.41,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0946, dtype=torch.float64)\n",
            "GAME = 3000, LOSSavg20 = 0.07816896, number of steps done in last= 17, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0468\n",
            "6\n",
            "1\n",
            "4\n",
            "2\n",
            "28\n",
            "7\n",
            "13\n",
            "2\n",
            "24\n",
            "28\n",
            "1\n",
            "12\n",
            "23\n",
            "18\n",
            "14\n",
            "1\n",
            "29\n",
            "3\n",
            "3\n",
            "6\n",
            "GOOD Q (left to the green): 0.8325000451629379 , BAD Q (under the red) 0.07291448044681559 , BAD Q 2(left of the red) 0.4345520453857642\n",
            "GOOD V (left to the green): 0.8325000451629379 , BAD V (under the red) 0.4345520453857642\n",
            "MSE_value= tensor(0.0960, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.14,1.09)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.09)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.28,3.85)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.38,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.77,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.64,6.65,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.41,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0960, dtype=torch.float64)\n",
            "GAME = 3020, LOSSavg20 = 0.07846590, number of steps done in last= 6, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0472\n",
            "18\n",
            "1\n",
            "6\n",
            "15\n",
            "1\n",
            "8\n",
            "99\n",
            "33\n",
            "11\n",
            "36\n",
            "60\n",
            "28\n",
            "5\n",
            "10\n",
            "12\n",
            "39\n",
            "11\n",
            "7\n",
            "3\n",
            "9\n",
            "GOOD Q (left to the green): 0.8166058624380703 , BAD Q (under the red) 0.11435816190685744 , BAD Q 2(left of the red) 0.40862760410104726\n",
            "GOOD V (left to the green): 0.8166058624380703 , BAD V (under the red) 0.40862760410104726\n",
            "MSE_value= tensor(0.0992, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,1.10)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.08)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.28,3.87)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.42,-1.92)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.42,1.77,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.66,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.85,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0992, dtype=torch.float64)\n",
            "GAME = 3040, LOSSavg20 = 0.07872354, number of steps done in last= 9, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0496\n",
            "60\n",
            "20\n",
            "2\n",
            "7\n",
            "33\n",
            "9\n",
            "10\n",
            "1\n",
            "30\n",
            "7\n",
            "7\n",
            "24\n",
            "15\n",
            "15\n",
            "1\n",
            "12\n",
            "27\n",
            "8\n",
            "8\n",
            "2\n",
            "GOOD Q (left to the green): 0.8107558896168383 , BAD Q (under the red) 0.10764585862115594 , BAD Q 2(left of the red) 0.37478778299338916\n",
            "GOOD V (left to the green): 0.8107558896168383 , BAD V (under the red) 0.37478778299338916\n",
            "MSE_value= tensor(0.1044, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.13,1.11)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.09)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.27,3.88)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.43,-1.90)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.78,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.67,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.83,-5.46,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1044, dtype=torch.float64)\n",
            "GAME = 3060, LOSSavg20 = 0.07874012, number of steps done in last= 2, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0494\n",
            "8\n",
            "2\n",
            "88\n",
            "7\n",
            "45\n",
            "6\n",
            "30\n",
            "32\n",
            "23\n",
            "34\n",
            "15\n",
            "8\n",
            "10\n",
            "6\n",
            "19\n",
            "1\n",
            "33\n",
            "46\n",
            "25\n",
            "24\n",
            "GOOD Q (left to the green): 0.8498592612644438 , BAD Q (under the red) 0.013088297629598011 , BAD Q 2(left of the red) 0.40084187995119464\n",
            "GOOD V (left to the green): 0.8498592612644438 , BAD V (under the red) 0.40084187995119464\n",
            "MSE_value= tensor(0.1184, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.15,1.09)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.12)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.30,3.87)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.45,-1.91)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.80,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.68,6.65,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.83,-5.48,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1184, dtype=torch.float64)\n",
            "GAME = 3080, LOSSavg20 = 0.07903099, number of steps done in last= 24, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0494\n",
            "33\n",
            "18\n",
            "1\n",
            "10\n",
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            "83\n",
            "23\n",
            "1\n",
            "31\n",
            "31\n",
            "67\n",
            "GOOD Q (left to the green): 0.8517019294915337 , BAD Q (under the red) 0.00025291867862375916 , BAD Q 2(left of the red) 0.40070969777588683\n",
            "GOOD V (left to the green): 0.8517019294915337 , BAD V (under the red) 0.40070969777588683\n",
            "MSE_value= tensor(0.1222, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.15,1.08)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.94,-2.12)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.30,3.87)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.47,-1.90)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.81,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.69,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.82,-5.50,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1222, dtype=torch.float64)\n",
            "GAME = 3100, LOSSavg20 = 0.07913481, number of steps done in last= 67, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0498\n",
            "5\n",
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            "65\n",
            "1\n",
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            "1\n",
            "41\n",
            "2\n",
            "12\n",
            "GOOD Q (left to the green): 0.8426559177211622 , BAD Q (under the red) 0.12209238346232898 , BAD Q 2(left of the red) 0.37680124179974184\n",
            "GOOD V (left to the green): 0.8426559177211622 , BAD V (under the red) 0.37680124179974184\n",
            "MSE_value= tensor(0.0959, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.16,1.12)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.32,3.86)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.44,-1.96)\n",
            "\n",
            "────────────╭●─────────Rot(0.11,-1.24,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.41,1.81,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.64,6.70,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.85,-5.48,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0959, dtype=torch.float64)\n",
            "GAME = 3120, LOSSavg20 = 0.07936871, number of steps done in last= 12, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0494\n",
            "22\n",
            "9\n",
            "11\n",
            "2\n",
            "3\n",
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            "4\n",
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            "18\n",
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            "28\n",
            "24\n",
            "11\n",
            "4\n",
            "43\n",
            "4\n",
            "6\n",
            "GOOD Q (left to the green): 0.8321153861776199 , BAD Q (under the red) 0.16025381722209842 , BAD Q 2(left of the red) 0.34536757777307686\n",
            "GOOD V (left to the green): 0.8321153861776199 , BAD V (under the red) 0.34536757777307686\n",
            "MSE_value= tensor(0.0920, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.16,1.14)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.32,3.87)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.43,-1.97)\n",
            "\n",
            "────────────╭●─────────Rot(0.12,-1.23,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.39,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.65,6.68,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.87,-5.47,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0920, dtype=torch.float64)\n",
            "GAME = 3140, LOSSavg20 = 0.07946111, number of steps done in last= 6, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0496\n",
            "25\n",
            "35\n",
            "1\n",
            "68\n",
            "31\n",
            "2\n",
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            "2\n",
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            "49\n",
            "1\n",
            "7\n",
            "GOOD Q (left to the green): 0.8149067851138463 , BAD Q (under the red) 0.15871460621839661 , BAD Q 2(left of the red) 0.32426778703067327\n",
            "GOOD V (left to the green): 0.8149067851138463 , BAD V (under the red) 0.32426778703067327\n",
            "MSE_value= tensor(0.1013, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.17,1.15)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.12)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.33,3.90)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.44,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.12,-1.23,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.38,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.69,6.65,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.86,-5.48,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1013, dtype=torch.float64)\n",
            "GAME = 3160, LOSSavg20 = 0.07946664, number of steps done in last= 7, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0510\n",
            "29\n",
            "44\n",
            "76\n",
            "4\n",
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            "14\n",
            "8\n",
            "31\n",
            "8\n",
            "56\n",
            "6\n",
            "1\n",
            "GOOD Q (left to the green): 0.770906769920467 , BAD Q (under the red) 0.15530793031618995 , BAD Q 2(left of the red) 0.2932676841716579\n",
            "GOOD V (left to the green): 0.770906769920467 , BAD V (under the red) 0.2932676841716579\n",
            "MSE_value= tensor(0.1036, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.18,1.16)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.12)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.34,3.94)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.39,-1.98)\n",
            "\n",
            "────────────╭●─────────Rot(0.12,-1.23,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.37,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.74,6.63,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.90,-5.42,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1036, dtype=torch.float64)\n",
            "GAME = 3180, LOSSavg20 = 0.07973427, number of steps done in last= 1, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0512\n",
            "1\n",
            "8\n",
            "43\n",
            "1\n",
            "13\n",
            "1\n",
            "12\n",
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            "4\n",
            "39\n",
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            "7\n",
            "5\n",
            "98\n",
            "70\n",
            "32\n",
            "12\n",
            "48\n",
            "GOOD Q (left to the green): 0.7657131733628888 , BAD Q (under the red) 0.13043324877592494 , BAD Q 2(left of the red) 0.2717082938825235\n",
            "GOOD V (left to the green): 0.7657131733628888 , BAD V (under the red) 0.2717082938825235\n",
            "MSE_value= tensor(0.1098, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.18,1.16)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.87,-2.13)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.32,3.96)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.37,-1.98)\n",
            "\n",
            "────────────╭●─────────Rot(0.11,-1.24,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.35,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.77,6.60,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.90,-5.41,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1098, dtype=torch.float64)\n",
            "GAME = 3200, LOSSavg20 = 0.07980360, number of steps done in last= 48, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0521\n",
            "3\n",
            "7\n",
            "1\n",
            "29\n",
            "27\n",
            "81\n",
            "6\n",
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            "1\n",
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            "1\n",
            "5\n",
            "2\n",
            "1\n",
            "57\n",
            "18\n",
            "18\n",
            "GOOD Q (left to the green): 0.7689132608272 , BAD Q (under the red) 0.08620394730604686 , BAD Q 2(left of the red) 0.3161664639878835\n",
            "GOOD V (left to the green): 0.7689132608272 , BAD V (under the red) 0.3161664639878835\n",
            "MSE_value= tensor(0.1150, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.19,1.15)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.13)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.31,3.93)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.39,-1.96)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.36,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.77,6.62,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.87,-5.43,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1150, dtype=torch.float64)\n",
            "GAME = 3220, LOSSavg20 = 0.07987411, number of steps done in last= 18, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0528\n",
            "83\n",
            "1\n",
            "3\n",
            "2\n",
            "10\n",
            "8\n",
            "45\n",
            "12\n",
            "26\n",
            "47\n",
            "13\n",
            "5\n",
            "13\n",
            "1\n",
            "3\n",
            "19\n",
            "10\n",
            "47\n",
            "4\n",
            "9\n",
            "GOOD Q (left to the green): 0.7666636441184969 , BAD Q (under the red) 0.17627319923725399 , BAD Q 2(left of the red) 0.33725847577914286\n",
            "GOOD V (left to the green): 0.7666636441184969 , BAD V (under the red) 0.33725847577914286\n",
            "MSE_value= tensor(0.1017, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.18,1.19)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.10)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.33,3.92)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.40,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.12,-1.23,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.37,1.80,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.75,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.86,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1017, dtype=torch.float64)\n",
            "GAME = 3240, LOSSavg20 = 0.08012472, number of steps done in last= 9, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0530\n",
            "8\n",
            "5\n",
            "5\n",
            "11\n",
            "1\n",
            "2\n",
            "29\n",
            "35\n",
            "30\n",
            "4\n",
            "5\n",
            "4\n",
            "57\n",
            "3\n",
            "9\n",
            "9\n",
            "2\n",
            "1\n",
            "5\n",
            "4\n",
            "GOOD Q (left to the green): 0.7654821806369654 , BAD Q (under the red) 0.18543043686125243 , BAD Q 2(left of the red) 0.31654823879018995\n",
            "GOOD V (left to the green): 0.7654821806369654 , BAD V (under the red) 0.31654823879018995\n",
            "MSE_value= tensor(0.1024, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.19,1.20)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.88,-2.11)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.34,3.93)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.39,-1.97)\n",
            "\n",
            "────────────╭●─────────Rot(0.12,-1.23,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.35,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.77,6.63,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.42,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1024, dtype=torch.float64)\n",
            "GAME = 3260, LOSSavg20 = 0.08036429, number of steps done in last= 4, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0532\n",
            "38\n",
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            "1\n",
            "108\n",
            "GOOD Q (left to the green): 0.7825796904233077 , BAD Q (under the red) 0.10331004496520291 , BAD Q 2(left of the red) 0.3163824028200887\n",
            "GOOD V (left to the green): 0.7825796904233077 , BAD V (under the red) 0.3163824028200887\n",
            "MSE_value= tensor(0.1060, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.21,1.18)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.15)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.35,3.92)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.37,-2.00)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.33,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.76,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.90,-5.41,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1060, dtype=torch.float64)\n",
            "GAME = 3280, LOSSavg20 = 0.08053307, number of steps done in last= 108, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0559\n",
            "5\n",
            "75\n",
            "22\n",
            "4\n",
            "31\n",
            "38\n",
            "5\n",
            "17\n",
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            "35\n",
            "3\n",
            "21\n",
            "10\n",
            "9\n",
            "34\n",
            "13\n",
            "1\n",
            "14\n",
            "GOOD Q (left to the green): 0.794460355213191 , BAD Q (under the red) 0.12588981676765992 , BAD Q 2(left of the red) 0.3276652007919667\n",
            "GOOD V (left to the green): 0.794460355213191 , BAD V (under the red) 0.3276652007919667\n",
            "MSE_value= tensor(0.0949, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.21,1.19)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.15)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.36,3.92)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.35,-2.02)\n",
            "\n",
            "────────────╭●─────────Rot(0.11,-1.24,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.32,1.84,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.76,6.66,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.92,-5.39,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0949, dtype=torch.float64)\n",
            "GAME = 3300, LOSSavg20 = 0.08078680, number of steps done in last= 14, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0562\n",
            "32\n",
            "1\n",
            "17\n",
            "32\n",
            "21\n",
            "51\n",
            "8\n",
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            "15\n",
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            "19\n",
            "84\n",
            "2\n",
            "20\n",
            "25\n",
            "16\n",
            "2\n",
            "14\n",
            "34\n",
            "GOOD Q (left to the green): 0.7634714066748953 , BAD Q (under the red) 0.108721426630356 , BAD Q 2(left of the red) 0.3684797556524702\n",
            "GOOD V (left to the green): 0.7634714066748953 , BAD V (under the red) 0.3684797556524702\n",
            "MSE_value= tensor(0.0935, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.21,1.18)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.13)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.36,3.91)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.33,-2.03)\n",
            "\n",
            "────────────╭●─────────Rot(0.11,-1.24,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.35,1.80,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.76,6.66,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.38,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0935, dtype=torch.float64)\n",
            "GAME = 3320, LOSSavg20 = 0.08099080, number of steps done in last= 34, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0571\n",
            "11\n",
            "8\n",
            "13\n",
            "21\n",
            "37\n",
            "8\n",
            "10\n",
            "53\n",
            "16\n",
            "6\n",
            "13\n",
            "1\n",
            "2\n",
            "132\n",
            "2\n",
            "14\n",
            "11\n",
            "1\n",
            "17\n",
            "2\n",
            "GOOD Q (left to the green): 0.7792984013775432 , BAD Q (under the red) 0.12677052796170685 , BAD Q 2(left of the red) 0.3789677516705101\n",
            "GOOD V (left to the green): 0.7792984013775432 , BAD V (under the red) 0.3789677516705101\n",
            "MSE_value= tensor(0.0934, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.20,1.19)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.13)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.35,3.91)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.35,-2.01)\n",
            "\n",
            "────────────╭●─────────Rot(0.11,-1.24,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.36,1.80,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.77,6.66,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.92,-5.39,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.0934, dtype=torch.float64)\n",
            "GAME = 3340, LOSSavg20 = 0.08129460, number of steps done in last= 2, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0573\n",
            "48\n",
            "53\n",
            "34\n",
            "5\n",
            "96\n",
            "29\n",
            "18\n",
            "2\n",
            "1\n",
            "67\n",
            "1\n",
            "21\n",
            "19\n",
            "3\n",
            "89\n",
            "21\n",
            "11\n",
            "5\n",
            "3\n",
            "5\n",
            "GOOD Q (left to the green): 0.800553462052175 , BAD Q (under the red) 0.13695318688668245 , BAD Q 2(left of the red) 0.3645651850617092\n",
            "GOOD V (left to the green): 0.800553462052175 , BAD V (under the red) 0.3645651850617092\n",
            "MSE_value= tensor(0.1024, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.20,1.20)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.14)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.36,3.91)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.40,-1.98)\n",
            "\n",
            "────────────╭●─────────Rot(0.11,-1.24,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.34,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.78,6.65,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.88,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1024, dtype=torch.float64)\n",
            "GAME = 3360, LOSSavg20 = 0.08147297, number of steps done in last= 5, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0581\n",
            "50\n",
            "15\n",
            "3\n",
            "4\n",
            "1\n",
            "13\n",
            "4\n",
            "72\n",
            "2\n",
            "27\n",
            "3\n",
            "4\n",
            "3\n",
            "20\n",
            "10\n",
            "11\n",
            "5\n",
            "28\n",
            "6\n",
            "32\n",
            "GOOD Q (left to the green): 0.7874371528833357 , BAD Q (under the red) 0.1554104897728747 , BAD Q 2(left of the red) 0.3465613351032335\n",
            "GOOD V (left to the green): 0.7874371528833357 , BAD V (under the red) 0.3465613351032335\n",
            "MSE_value= tensor(0.1075, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.19,1.20)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.91,-2.13)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.34,3.93)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.41,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.11,-1.24,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.36,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.82,6.62,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.86,-5.45,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1075, dtype=torch.float64)\n",
            "GAME = 3380, LOSSavg20 = 0.08151095, number of steps done in last= 32, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0565\n",
            "23\n",
            "56\n",
            "9\n",
            "9\n",
            "7\n",
            "25\n",
            "1\n",
            "6\n",
            "30\n",
            "37\n",
            "1\n",
            "11\n",
            "68\n",
            "24\n",
            "1\n",
            "19\n",
            "7\n",
            "9\n",
            "22\n",
            "5\n",
            "GOOD Q (left to the green): 0.7564935625785874 , BAD Q (under the red) 0.10725287038235665 , BAD Q 2(left of the red) 0.3205660779994563\n",
            "GOOD V (left to the green): 0.7564935625785874 , BAD V (under the red) 0.3205660779994563\n",
            "MSE_value= tensor(0.1209, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.20,1.19)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.14)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.36,3.97)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.40,-1.96)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.35,1.83,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.85,6.62,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.87,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1209, dtype=torch.float64)\n",
            "GAME = 3400, LOSSavg20 = 0.08141197, number of steps done in last= 5, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0563\n",
            "66\n",
            "10\n",
            "33\n",
            "16\n",
            "65\n",
            "58\n",
            "11\n",
            "4\n",
            "90\n",
            "24\n",
            "38\n",
            "59\n",
            "6\n",
            "12\n",
            "53\n",
            "8\n",
            "8\n",
            "26\n",
            "5\n",
            "5\n",
            "GOOD Q (left to the green): 0.6920648018738791 , BAD Q (under the red) -0.045883112317941344 , BAD Q 2(left of the red) 0.36356596071021674\n",
            "GOOD V (left to the green): 0.6920648018738791 , BAD V (under the red) 0.36356596071021674\n",
            "MSE_value= tensor(0.1326, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.24,1.16)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.96,-2.14)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.38,3.96)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.33,-2.02)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.29,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.40,1.79,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.87,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.93,-5.38,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1326, dtype=torch.float64)\n",
            "GAME = 3420, LOSSavg20 = 0.08162330, number of steps done in last= 5, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0573\n",
            "21\n",
            "1\n",
            "16\n",
            "10\n",
            "10\n",
            "2\n",
            "4\n",
            "21\n",
            "2\n",
            "14\n",
            "2\n",
            "4\n",
            "4\n",
            "49\n",
            "26\n",
            "5\n",
            "12\n",
            "11\n",
            "3\n",
            "1\n",
            "GOOD Q (left to the green): 0.6759076471373373 , BAD Q (under the red) -0.06413871008079575 , BAD Q 2(left of the red) 0.3741634662092954\n",
            "GOOD V (left to the green): 0.6759076471373373 , BAD V (under the red) 0.3741634662092954\n",
            "MSE_value= tensor(0.1277, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.23,1.15)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.97,-2.13)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.38,3.96)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.34,-2.02)\n",
            "\n",
            "────────────╭●─────────Rot(0.05,-1.30,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.43,1.77,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.88,6.65,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.91,-5.39,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1277, dtype=torch.float64)\n",
            "GAME = 3440, LOSSavg20 = 0.08162183, number of steps done in last= 1, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0575\n",
            "3\n",
            "9\n",
            "1\n",
            "26\n",
            "1\n",
            "14\n",
            "47\n",
            "21\n",
            "1\n",
            "51\n",
            "10\n",
            "13\n",
            "12\n",
            "5\n",
            "2\n",
            "17\n",
            "5\n",
            "4\n",
            "2\n",
            "9\n",
            "GOOD Q (left to the green): 0.6974083212363661 , BAD Q (under the red) 0.005347464219039533 , BAD Q 2(left of the red) 0.3469861684604014\n",
            "GOOD V (left to the green): 0.6974083212363661 , BAD V (under the red) 0.3469861684604014\n",
            "MSE_value= tensor(0.1281, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.21,1.17)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.95,-2.12)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.37,3.98)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.34,-2.02)\n",
            "\n",
            "────────────╭●─────────Rot(0.06,-1.29,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.42,1.78,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.91,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.92,-5.39,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1281, dtype=torch.float64)\n",
            "GAME = 3460, LOSSavg20 = 0.08183660, number of steps done in last= 9, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0597\n",
            "18\n",
            "5\n",
            "3\n",
            "2\n",
            "12\n",
            "18\n",
            "4\n",
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            "3\n",
            "42\n",
            "23\n",
            "4\n",
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            "1\n",
            "31\n",
            "41\n",
            "6\n",
            "34\n",
            "4\n",
            "1\n",
            "GOOD Q (left to the green): 0.762317335347282 , BAD Q (under the red) 0.06512724221408472 , BAD Q 2(left of the red) 0.3273002639745478\n",
            "GOOD V (left to the green): 0.762317335347282 , BAD V (under the red) 0.3273002639745478\n",
            "MSE_value= tensor(0.1110, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.18,1.19)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.13)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.34,3.97)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.33,-2.01)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.39,1.81,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.93,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.91,-5.38,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1110, dtype=torch.float64)\n",
            "GAME = 3480, LOSSavg20 = 0.08193322, number of steps done in last= 1, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0611\n",
            "8\n",
            "1\n",
            "41\n",
            "2\n",
            "108\n",
            "8\n",
            "13\n",
            "2\n",
            "3\n",
            "7\n",
            "3\n",
            "3\n",
            "2\n",
            "16\n",
            "33\n",
            "27\n",
            "1\n",
            "4\n",
            "30\n",
            "36\n",
            "GOOD Q (left to the green): 0.7907418484009741 , BAD Q (under the red) 0.0487216509405865 , BAD Q 2(left of the red) 0.2840061580864345\n",
            "GOOD V (left to the green): 0.7907418484009741 , BAD V (under the red) 0.2840061580864345\n",
            "MSE_value= tensor(0.1206, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.18,1.18)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.92,-2.17)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.33,3.99)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.35,-1.99)\n",
            "\n",
            "────────────╭●─────────Rot(0.07,-1.28,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.35,1.85,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.95,6.62,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.89,-5.40,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1206, dtype=torch.float64)\n",
            "GAME = 3500, LOSSavg20 = 0.08200924, number of steps done in last= 36, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0609\n",
            "4\n",
            "2\n",
            "3\n",
            "24\n",
            "13\n",
            "7\n",
            "14\n",
            "5\n",
            "1\n",
            "25\n",
            "2\n",
            "11\n",
            "1\n",
            "76\n",
            "4\n",
            "25\n",
            "33\n",
            "19\n",
            "16\n",
            "59\n",
            "GOOD Q (left to the green): 0.8552821851233345 , BAD Q (under the red) 0.11073675017947521 , BAD Q 2(left of the red) 0.2551110009456425\n",
            "GOOD V (left to the green): 0.8552821851233345 , BAD V (under the red) 0.2551110009456425\n",
            "MSE_value= tensor(0.1098, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.16,1.19)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.89,-2.20)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.30,3.98)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.36,-1.98)\n",
            "\n",
            "────────────╭●─────────Rot(0.09,-1.26,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.31,1.88,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.92,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.87,-5.41,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1098, dtype=torch.float64)\n",
            "GAME = 3520, LOSSavg20 = 0.08203267, number of steps done in last= 59, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0633\n",
            "15\n",
            "42\n",
            "65\n",
            "4\n",
            "16\n",
            "26\n",
            "1\n",
            "52\n",
            "2\n",
            "6\n",
            "27\n",
            "25\n",
            "7\n",
            "38\n",
            "24\n",
            "1\n",
            "44\n",
            "2\n",
            "48\n",
            "6\n",
            "GOOD Q (left to the green): 0.8655601437371678 , BAD Q (under the red) 0.13063567629592276 , BAD Q 2(left of the red) 0.2885773440285078\n",
            "GOOD V (left to the green): 0.8655601437371678 , BAD V (under the red) 0.2885773440285078\n",
            "MSE_value= tensor(0.1067, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.16,1.19)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.90,-2.20)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.29,3.96)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.37,-1.96)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.31,1.87,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.91,6.64,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.85,-5.42,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1067, dtype=torch.float64)\n",
            "GAME = 3540, LOSSavg20 = 0.08205233, number of steps done in last= 6, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0636\n",
            "77\n",
            "6\n",
            "17\n",
            "25\n",
            "8\n",
            "12\n",
            "2\n",
            "59\n",
            "8\n",
            "4\n",
            "5\n",
            "19\n",
            "18\n",
            "2\n",
            "2\n",
            "6\n",
            "46\n",
            "2\n",
            "1\n",
            "14\n",
            "GOOD Q (left to the green): 0.8546158213276291 , BAD Q (under the red) 0.1036262403004028 , BAD Q 2(left of the red) 0.3364432383586834\n",
            "GOOD V (left to the green): 0.8546158213276291 , BAD V (under the red) 0.3364432383586834\n",
            "MSE_value= tensor(0.1077, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.17,1.18)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.93,-2.20)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.30,3.94)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.38,-1.95)\n",
            "\n",
            "────────────╭●─────────Rot(0.10,-1.25,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.33,1.85,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.88,6.67,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.84,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1077, dtype=torch.float64)\n",
            "GAME = 3560, LOSSavg20 = 0.08215493, number of steps done in last= 14, last_episode's_last_state = 7, epsilon = 0.0500, game_rewardavg20 = -0.0634\n",
            "11\n",
            "15\n",
            "5\n",
            "1\n",
            "88\n",
            "73\n",
            "7\n",
            "17\n",
            "41\n",
            "13\n",
            "3\n",
            "6\n",
            "26\n",
            "9\n",
            "6\n",
            "3\n",
            "14\n",
            "9\n",
            "52\n",
            "36\n",
            "GOOD Q (left to the green): 0.825028303873804 , BAD Q (under the red) 0.05824370024377706 , BAD Q 2(left of the red) 0.36766838098834353\n",
            "GOOD V (left to the green): 0.825028303873804 , BAD V (under the red) 0.36766838098834353\n",
            "MSE_value= tensor(0.1133, dtype=torch.float64)\n",
            "0: ──RX(3.14)──RZ(3.14)─╭●──Rot(0.20,-1.16,1.16)──RX(3.14)───────────────RZ(3.14)─────────────\n",
            "1: ──RX(0.00)──RZ(0.00)─╰X─╭●─────────────────────Rot(0.19,-6.94,-2.19)──RX(0.00)─────────────\n",
            "2: ──RX(0.00)──RZ(0.00)────╰X────────────────────╭●──────────────────────Rot(-0.54,-1.28,3.93)\n",
            "3: ──RX(3.14)──RZ(3.14)──────────────────────────╰X──────────────────────Rot(0.21,-5.39,-1.94)\n",
            "\n",
            "────────────╭●─────────Rot(0.08,-1.27,0.09)───────────────────────────────────────────────┤  <Z>\n",
            "───RZ(0.00)─╰X────────╭●─────────────────────Rot(-4.37,1.82,-0.10)────────────────────────┤  <Z>\n",
            "───RX(0.00)──RZ(0.00)─╰X────────────────────╭●──────────────────────Rot(-0.88,6.66,-0.03)─┤  <Z>\n",
            "───RX(3.14)──RZ(3.14)───────────────────────╰X──────────────────────Rot(1.82,-5.44,0.31)──┤  <Z>\n",
            "Learning rate: 0.00078125 MSE= tensor(0.1133, dtype=torch.float64)\n",
            "GAME = 3580, LOSSavg20 = 0.08224185, number of steps done in last= 36, last_episode's_last_state = 11, epsilon = 0.0500, game_rewardavg20 = -0.0637\n",
            "22\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[66], line 109\u001b[0m\n\u001b[0;32m    106\u001b[0m   y \u001b[38;5;241m=\u001b[39m reward_j \u001b[38;5;241m+\u001b[39m Gamma \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mmax\u001b[39m(variational_classifier(var_Q_circuit \u001b[38;5;241m=\u001b[39m var_target_Q_circuit, angles \u001b[38;5;241m=\u001b[39m decimalToBinaryFixLength(\u001b[38;5;241m4\u001b[39m,s_jf)))\n\u001b[0;32m    107\u001b[0m \u001b[38;5;66;03m#if s_j==10 and action_j ==1:\u001b[39;00m\n\u001b[0;32m    108\u001b[0m   \u001b[38;5;66;03m#print(y,Q(phi_j)[action_j]\u001b[39;00m\n\u001b[1;32m--> 109\u001b[0m pred\u001b[38;5;241m=\u001b[39mvariational_classifier(var_Q_circuit \u001b[38;5;241m=\u001b[39m var_Q_circuit, angles \u001b[38;5;241m=\u001b[39m decimalToBinaryFixLength(\u001b[38;5;241m4\u001b[39m,s_jf))[action_j]\n\u001b[0;32m    110\u001b[0m \u001b[38;5;66;03m#print(pred,pred.type)\u001b[39;00m\n\u001b[0;32m    111\u001b[0m loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39mloss_fnc(pred,y) \u001b[38;5;241m/\u001b[39m len_batch\n",
            "Cell \u001b[1;32mIn[60], line 18\u001b[0m, in \u001b[0;36mvariational_classifier\u001b[1;34m(var_Q_circuit, angles)\u001b[0m\n\u001b[0;32m     13\u001b[0m weights \u001b[38;5;241m=\u001b[39m var_Q_circuit\n\u001b[0;32m     14\u001b[0m \u001b[38;5;66;03m#print(\"WEIGHTS\", weights)\u001b[39;00m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;66;03m#print()\u001b[39;00m\n\u001b[0;32m     16\u001b[0m \u001b[38;5;66;03m#print()\u001b[39;00m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;66;03m#raw_output=torch.tensor(circuit(weights,angles=angles),requires_grad=True).type(dtype) * 2.0-1.0\u001b[39;00m\n\u001b[1;32m---> 18\u001b[0m raw_output\u001b[38;5;241m=\u001b[39mcircuit(weights,angles\u001b[38;5;241m=\u001b[39mangles)\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(raw_output)):\n\u001b[0;32m     20\u001b[0m \traw_output[i]\u001b[38;5;241m=\u001b[39mraw_output[i]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m3.0\u001b[39m\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\workflow\\qnode.py:1164\u001b[0m, in \u001b[0;36mQNode.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1162\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m qml\u001b[38;5;241m.\u001b[39mcapture\u001b[38;5;241m.\u001b[39menabled():\n\u001b[0;32m   1163\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m qml\u001b[38;5;241m.\u001b[39mcapture\u001b[38;5;241m.\u001b[39mqnode_call(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m-> 1164\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_impl_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\workflow\\qnode.py:1150\u001b[0m, in \u001b[0;36mQNode._impl_call\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1147\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_gradient_fn(shots\u001b[38;5;241m=\u001b[39moverride_shots, tape\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tape)\n\u001b[0;32m   1149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1150\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_execution_component(args, kwargs, override_shots\u001b[38;5;241m=\u001b[39moverride_shots)\n\u001b[0;32m   1151\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m   1152\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m old_interface \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\workflow\\qnode.py:1103\u001b[0m, in \u001b[0;36mQNode._execution_component\u001b[1;34m(self, args, kwargs, override_shots)\u001b[0m\n\u001b[0;32m   1100\u001b[0m _prune_dynamic_transform(full_transform_program, inner_transform_program)\n\u001b[0;32m   1102\u001b[0m \u001b[38;5;66;03m# pylint: disable=unexpected-keyword-arg\u001b[39;00m\n\u001b[1;32m-> 1103\u001b[0m res \u001b[38;5;241m=\u001b[39m qml\u001b[38;5;241m.\u001b[39mexecute(\n\u001b[0;32m   1104\u001b[0m     (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tape,),\n\u001b[0;32m   1105\u001b[0m     device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice,\n\u001b[0;32m   1106\u001b[0m     gradient_fn\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgradient_fn,\n\u001b[0;32m   1107\u001b[0m     interface\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minterface,\n\u001b[0;32m   1108\u001b[0m     transform_program\u001b[38;5;241m=\u001b[39mfull_transform_program,\n\u001b[0;32m   1109\u001b[0m     inner_transform\u001b[38;5;241m=\u001b[39minner_transform_program,\n\u001b[0;32m   1110\u001b[0m     config\u001b[38;5;241m=\u001b[39mconfig,\n\u001b[0;32m   1111\u001b[0m     gradient_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgradient_kwargs,\n\u001b[0;32m   1112\u001b[0m     override_shots\u001b[38;5;241m=\u001b[39moverride_shots,\n\u001b[0;32m   1113\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexecute_kwargs,\n\u001b[0;32m   1114\u001b[0m )\n\u001b[0;32m   1115\u001b[0m res \u001b[38;5;241m=\u001b[39m res[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m   1117\u001b[0m \u001b[38;5;66;03m# convert result to the interface in case the qfunc has no parameters\u001b[39;00m\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\workflow\\execution.py:666\u001b[0m, in \u001b[0;36mexecute\u001b[1;34m(tapes, device, gradient_fn, interface, transform_program, inner_transform, config, grad_on_execution, gradient_kwargs, cache, cachesize, max_diff, override_shots, expand_fn, max_expansion, device_batch_transform, device_vjp, mcm_config)\u001b[0m\n\u001b[0;32m    664\u001b[0m \u001b[38;5;66;03m# Exiting early if we do not need to deal with an interface boundary\u001b[39;00m\n\u001b[0;32m    665\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m no_interface_boundary_required:\n\u001b[1;32m--> 666\u001b[0m     results \u001b[38;5;241m=\u001b[39m inner_execute(tapes)\n\u001b[0;32m    667\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m post_processing(results)\n\u001b[0;32m    669\u001b[0m _grad_on_execution \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\workflow\\execution.py:316\u001b[0m, in \u001b[0;36m_make_inner_execute.<locals>.inner_execute\u001b[1;34m(tapes, **_)\u001b[0m\n\u001b[0;32m    313\u001b[0m     transformed_tapes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(expand_fn(t) \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m transformed_tapes)\n\u001b[0;32m    315\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m transformed_tapes:\n\u001b[1;32m--> 316\u001b[0m     results \u001b[38;5;241m=\u001b[39m device_execution(transformed_tapes)\n\u001b[0;32m    317\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    318\u001b[0m     results \u001b[38;5;241m=\u001b[39m ()\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\devices\\modifiers\\simulator_tracking.py:30\u001b[0m, in \u001b[0;36m_track_execute.<locals>.execute\u001b[1;34m(self, circuits, execution_config)\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(untracked_execute)\n\u001b[0;32m     29\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mexecute\u001b[39m(\u001b[38;5;28mself\u001b[39m, circuits, execution_config\u001b[38;5;241m=\u001b[39mDefaultExecutionConfig):\n\u001b[1;32m---> 30\u001b[0m     results \u001b[38;5;241m=\u001b[39m untracked_execute(\u001b[38;5;28mself\u001b[39m, circuits, execution_config)\n\u001b[0;32m     31\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(circuits, QuantumScript):\n\u001b[0;32m     32\u001b[0m         batch \u001b[38;5;241m=\u001b[39m (circuits,)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\devices\\modifiers\\single_tape_support.py:32\u001b[0m, in \u001b[0;36m_make_execute.<locals>.execute\u001b[1;34m(self, circuits, execution_config)\u001b[0m\n\u001b[0;32m     30\u001b[0m     is_single_circuit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m     31\u001b[0m     circuits \u001b[38;5;241m=\u001b[39m (circuits,)\n\u001b[1;32m---> 32\u001b[0m results \u001b[38;5;241m=\u001b[39m batch_execute(\u001b[38;5;28mself\u001b[39m, circuits, execution_config)\n\u001b[0;32m     33\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m results[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m is_single_circuit \u001b[38;5;28;01melse\u001b[39;00m results\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\logging\\decorators.py:61\u001b[0m, in \u001b[0;36mlog_string_debug_func.<locals>.wrapper_entry\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     54\u001b[0m     s_caller \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m::L\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\n\u001b[0;32m     55\u001b[0m         [\u001b[38;5;28mstr\u001b[39m(i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39mgetouterframes(inspect\u001b[38;5;241m.\u001b[39mcurrentframe(), \u001b[38;5;241m2\u001b[39m)[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m3\u001b[39m]]\n\u001b[0;32m     56\u001b[0m     )\n\u001b[0;32m     57\u001b[0m     lgr\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[0;32m     58\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCalling \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mf_string\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00ms_caller\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     59\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_debug_log_kwargs,\n\u001b[0;32m     60\u001b[0m     )\n\u001b[1;32m---> 61\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\devices\\default_qubit.py:597\u001b[0m, in \u001b[0;36mDefaultQubit.execute\u001b[1;34m(self, circuits, execution_config)\u001b[0m\n\u001b[0;32m    594\u001b[0m prng_keys \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_prng_keys()[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(circuits))]\n\u001b[0;32m    596\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m max_workers \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 597\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(\n\u001b[0;32m    598\u001b[0m         _simulate_wrapper(\n\u001b[0;32m    599\u001b[0m             c,\n\u001b[0;32m    600\u001b[0m             {\n\u001b[0;32m    601\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrng\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_rng,\n\u001b[0;32m    602\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdebugger\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_debugger,\n\u001b[0;32m    603\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minterface\u001b[39m\u001b[38;5;124m\"\u001b[39m: interface,\n\u001b[0;32m    604\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state_cache,\n\u001b[0;32m    605\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprng_key\u001b[39m\u001b[38;5;124m\"\u001b[39m: _key,\n\u001b[0;32m    606\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmcm_method\u001b[39m\u001b[38;5;124m\"\u001b[39m: execution_config\u001b[38;5;241m.\u001b[39mmcm_config\u001b[38;5;241m.\u001b[39mmcm_method,\n\u001b[0;32m    607\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpostselect_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m: execution_config\u001b[38;5;241m.\u001b[39mmcm_config\u001b[38;5;241m.\u001b[39mpostselect_mode,\n\u001b[0;32m    608\u001b[0m             },\n\u001b[0;32m    609\u001b[0m         )\n\u001b[0;32m    610\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m c, _key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(circuits, prng_keys)\n\u001b[0;32m    611\u001b[0m     )\n\u001b[0;32m    613\u001b[0m vanilla_circuits \u001b[38;5;241m=\u001b[39m convert_to_numpy_parameters(circuits)[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m    614\u001b[0m seeds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_rng\u001b[38;5;241m.\u001b[39mintegers(\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m31\u001b[39m \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(vanilla_circuits))\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\devices\\default_qubit.py:598\u001b[0m, in \u001b[0;36m<genexpr>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    594\u001b[0m prng_keys \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_prng_keys()[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(circuits))]\n\u001b[0;32m    596\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m max_workers \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    597\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(\n\u001b[1;32m--> 598\u001b[0m         _simulate_wrapper(\n\u001b[0;32m    599\u001b[0m             c,\n\u001b[0;32m    600\u001b[0m             {\n\u001b[0;32m    601\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrng\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_rng,\n\u001b[0;32m    602\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdebugger\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_debugger,\n\u001b[0;32m    603\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minterface\u001b[39m\u001b[38;5;124m\"\u001b[39m: interface,\n\u001b[0;32m    604\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state_cache,\n\u001b[0;32m    605\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprng_key\u001b[39m\u001b[38;5;124m\"\u001b[39m: _key,\n\u001b[0;32m    606\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmcm_method\u001b[39m\u001b[38;5;124m\"\u001b[39m: execution_config\u001b[38;5;241m.\u001b[39mmcm_config\u001b[38;5;241m.\u001b[39mmcm_method,\n\u001b[0;32m    607\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpostselect_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m: execution_config\u001b[38;5;241m.\u001b[39mmcm_config\u001b[38;5;241m.\u001b[39mpostselect_mode,\n\u001b[0;32m    608\u001b[0m             },\n\u001b[0;32m    609\u001b[0m         )\n\u001b[0;32m    610\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m c, _key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(circuits, prng_keys)\n\u001b[0;32m    611\u001b[0m     )\n\u001b[0;32m    613\u001b[0m vanilla_circuits \u001b[38;5;241m=\u001b[39m convert_to_numpy_parameters(circuits)[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m    614\u001b[0m seeds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_rng\u001b[38;5;241m.\u001b[39mintegers(\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m31\u001b[39m \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m, size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(vanilla_circuits))\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\devices\\default_qubit.py:863\u001b[0m, in \u001b[0;36m_simulate_wrapper\u001b[1;34m(circuit, kwargs)\u001b[0m\n\u001b[0;32m    862\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_simulate_wrapper\u001b[39m(circuit, kwargs):\n\u001b[1;32m--> 863\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m simulate(circuit, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\logging\\decorators.py:61\u001b[0m, in \u001b[0;36mlog_string_debug_func.<locals>.wrapper_entry\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     54\u001b[0m     s_caller \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m::L\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\n\u001b[0;32m     55\u001b[0m         [\u001b[38;5;28mstr\u001b[39m(i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39mgetouterframes(inspect\u001b[38;5;241m.\u001b[39mcurrentframe(), \u001b[38;5;241m2\u001b[39m)[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m3\u001b[39m]]\n\u001b[0;32m     56\u001b[0m     )\n\u001b[0;32m     57\u001b[0m     lgr\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[0;32m     58\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCalling \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mf_string\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00ms_caller\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     59\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_debug_log_kwargs,\n\u001b[0;32m     60\u001b[0m     )\n\u001b[1;32m---> 61\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\devices\\qubit\\simulate.py:354\u001b[0m, in \u001b[0;36msimulate\u001b[1;34m(circuit, debugger, state_cache, **execution_kwargs)\u001b[0m\n\u001b[0;32m    351\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(results)\n\u001b[0;32m    353\u001b[0m ops_key, meas_key \u001b[38;5;241m=\u001b[39m jax_random_split(prng_key)\n\u001b[1;32m--> 354\u001b[0m state, is_state_batched \u001b[38;5;241m=\u001b[39m get_final_state(\n\u001b[0;32m    355\u001b[0m     circuit, debugger\u001b[38;5;241m=\u001b[39mdebugger, prng_key\u001b[38;5;241m=\u001b[39mops_key, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mexecution_kwargs\n\u001b[0;32m    356\u001b[0m )\n\u001b[0;32m    357\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m state_cache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    358\u001b[0m     state_cache[circuit\u001b[38;5;241m.\u001b[39mhash] \u001b[38;5;241m=\u001b[39m state\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\logging\\decorators.py:61\u001b[0m, in \u001b[0;36mlog_string_debug_func.<locals>.wrapper_entry\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     54\u001b[0m     s_caller \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m::L\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\n\u001b[0;32m     55\u001b[0m         [\u001b[38;5;28mstr\u001b[39m(i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39mgetouterframes(inspect\u001b[38;5;241m.\u001b[39mcurrentframe(), \u001b[38;5;241m2\u001b[39m)[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m3\u001b[39m]]\n\u001b[0;32m     56\u001b[0m     )\n\u001b[0;32m     57\u001b[0m     lgr\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[0;32m     58\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCalling \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mf_string\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00ms_caller\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     59\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_debug_log_kwargs,\n\u001b[0;32m     60\u001b[0m     )\n\u001b[1;32m---> 61\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\devices\\qubit\\simulate.py:174\u001b[0m, in \u001b[0;36mget_final_state\u001b[1;34m(circuit, debugger, **execution_kwargs)\u001b[0m\n\u001b[0;32m    172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(op, MidMeasureMP):\n\u001b[0;32m    173\u001b[0m     prng_key, key \u001b[38;5;241m=\u001b[39m jax_random_split(prng_key)\n\u001b[1;32m--> 174\u001b[0m state \u001b[38;5;241m=\u001b[39m apply_operation(\n\u001b[0;32m    175\u001b[0m     op,\n\u001b[0;32m    176\u001b[0m     state,\n\u001b[0;32m    177\u001b[0m     is_state_batched\u001b[38;5;241m=\u001b[39mis_state_batched,\n\u001b[0;32m    178\u001b[0m     debugger\u001b[38;5;241m=\u001b[39mdebugger,\n\u001b[0;32m    179\u001b[0m     prng_key\u001b[38;5;241m=\u001b[39mkey,\n\u001b[0;32m    180\u001b[0m     tape_shots\u001b[38;5;241m=\u001b[39mcircuit\u001b[38;5;241m.\u001b[39mshots,\n\u001b[0;32m    181\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mexecution_kwargs,\n\u001b[0;32m    182\u001b[0m )\n\u001b[0;32m    183\u001b[0m \u001b[38;5;66;03m# Handle postselection on mid-circuit measurements\u001b[39;00m\n\u001b[0;32m    184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(op, qml\u001b[38;5;241m.\u001b[39mProjector):\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\functools.py:909\u001b[0m, in \u001b[0;36msingledispatch.<locals>.wrapper\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m    905\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[0;32m    906\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfuncname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires at least \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    907\u001b[0m                     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1 positional argument\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m--> 909\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dispatch(args[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\devices\\qubit\\apply_operation.py:218\u001b[0m, in \u001b[0;36mapply_operation\u001b[1;34m(op, state, is_state_batched, debugger, **_)\u001b[0m\n\u001b[0;32m    152\u001b[0m \u001b[38;5;129m@singledispatch\u001b[39m\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_operation\u001b[39m(\n\u001b[0;32m    154\u001b[0m     op: qml\u001b[38;5;241m.\u001b[39moperation\u001b[38;5;241m.\u001b[39mOperator,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    158\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_,\n\u001b[0;32m    159\u001b[0m ):\n\u001b[0;32m    160\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Apply and operator to a given state.\u001b[39;00m\n\u001b[0;32m    161\u001b[0m \n\u001b[0;32m    162\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    216\u001b[0m \n\u001b[0;32m    217\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 218\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _apply_operation_default(op, state, is_state_batched, debugger)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\devices\\qubit\\apply_operation.py:228\u001b[0m, in \u001b[0;36m_apply_operation_default\u001b[1;34m(op, state, is_state_batched, debugger)\u001b[0m\n\u001b[0;32m    222\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"The default behaviour of apply_operation, accessed through the standard dispatch\u001b[39;00m\n\u001b[0;32m    223\u001b[0m \u001b[38;5;124;03mof apply_operation, as well as conditionally in other dispatches.\"\"\"\u001b[39;00m\n\u001b[0;32m    224\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    225\u001b[0m     \u001b[38;5;28mlen\u001b[39m(op\u001b[38;5;241m.\u001b[39mwires) \u001b[38;5;241m<\u001b[39m EINSUM_OP_WIRECOUNT_PERF_THRESHOLD\n\u001b[0;32m    226\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m math\u001b[38;5;241m.\u001b[39mndim(state) \u001b[38;5;241m<\u001b[39m EINSUM_STATE_WIRECOUNT_PERF_THRESHOLD\n\u001b[0;32m    227\u001b[0m ) \u001b[38;5;129;01mor\u001b[39;00m (op\u001b[38;5;241m.\u001b[39mbatch_size \u001b[38;5;129;01mand\u001b[39;00m is_state_batched):\n\u001b[1;32m--> 228\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m apply_operation_einsum(op, state, is_state_batched\u001b[38;5;241m=\u001b[39mis_state_batched)\n\u001b[0;32m    229\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m apply_operation_tensordot(op, state, is_state_batched\u001b[38;5;241m=\u001b[39mis_state_batched)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\devices\\qubit\\apply_operation.py:102\u001b[0m, in \u001b[0;36mapply_operation_einsum\u001b[1;34m(op, state, is_state_batched)\u001b[0m\n\u001b[0;32m     99\u001b[0m         op\u001b[38;5;241m.\u001b[39m_batch_size \u001b[38;5;241m=\u001b[39m batch_size  \u001b[38;5;66;03m# pylint:disable=protected-access\u001b[39;00m\n\u001b[0;32m    100\u001b[0m reshaped_mat \u001b[38;5;241m=\u001b[39m math\u001b[38;5;241m.\u001b[39mreshape(mat, new_mat_shape)\n\u001b[1;32m--> 102\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m math\u001b[38;5;241m.\u001b[39meinsum(einsum_indices, reshaped_mat, state)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\math\\multi_dispatch.py:554\u001b[0m, in \u001b[0;36meinsum\u001b[1;34m(indices, like, optimize, *operands)\u001b[0m\n\u001b[0;32m    552\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m like \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    553\u001b[0m     like \u001b[38;5;241m=\u001b[39m get_interface(\u001b[38;5;241m*\u001b[39moperands)\n\u001b[1;32m--> 554\u001b[0m operands \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcoerce(operands, like\u001b[38;5;241m=\u001b[39mlike)\n\u001b[0;32m    555\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m optimize \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m like \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    556\u001b[0m     \u001b[38;5;66;03m# torch einsum doesn't support the optimize keyword argument\u001b[39;00m\n\u001b[0;32m    557\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39meinsum(indices, \u001b[38;5;241m*\u001b[39moperands, like\u001b[38;5;241m=\u001b[39mlike)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\autoray\\autoray.py:81\u001b[0m, in \u001b[0;36mdo\u001b[1;34m(fn, like, *args, **kwargs)\u001b[0m\n\u001b[0;32m     79\u001b[0m backend \u001b[38;5;241m=\u001b[39m _choose_backend(fn, args, kwargs, like\u001b[38;5;241m=\u001b[39mlike)\n\u001b[0;32m     80\u001b[0m func \u001b[38;5;241m=\u001b[39m get_lib_fn(backend, fn)\n\u001b[1;32m---> 81\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\math\\single_dispatch.py:652\u001b[0m, in \u001b[0;36m_coerce_types_torch\u001b[1;34m(tensors)\u001b[0m\n\u001b[0;32m    650\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    651\u001b[0m     device \u001b[38;5;241m=\u001b[39m device_set\u001b[38;5;241m.\u001b[39mpop()\n\u001b[1;32m--> 652\u001b[0m     tensors \u001b[38;5;241m=\u001b[39m [torch\u001b[38;5;241m.\u001b[39mas_tensor(t, device\u001b[38;5;241m=\u001b[39mdevice) \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tensors]\n\u001b[0;32m    654\u001b[0m dtypes \u001b[38;5;241m=\u001b[39m {i\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m tensors}\n\u001b[0;32m    656\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(dtypes) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
            "File \u001b[1;32mc:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\math\\single_dispatch.py:652\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    650\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    651\u001b[0m     device \u001b[38;5;241m=\u001b[39m device_set\u001b[38;5;241m.\u001b[39mpop()\n\u001b[1;32m--> 652\u001b[0m     tensors \u001b[38;5;241m=\u001b[39m [torch\u001b[38;5;241m.\u001b[39mas_tensor(t, device\u001b[38;5;241m=\u001b[39mdevice) \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tensors]\n\u001b[0;32m    654\u001b[0m dtypes \u001b[38;5;241m=\u001b[39m {i\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m tensors}\n\u001b[0;32m    656\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(dtypes) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
            "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "LR_0 = 0.2\n",
        "LR_MIN = 0.001\n",
        "num_games = 20000\n",
        "max_time = 250\n",
        "epsilon_0=0.95\n",
        "N = 700 #Lenght of replay memory\n",
        "len_batch=1\n",
        "epsilon_min = 0.05\n",
        "C=200 #steps\n",
        "#alpha_0=0.01\n",
        "#alpha=alpha_0\n",
        "#alpha_min=0.003\n",
        "c=0\n",
        "count = 0\n",
        "D=[]\n",
        "\n",
        "\n",
        "best_reward =-10.0\n",
        "MAE_U =[]\n",
        "good_Q_print=0.0\n",
        "good_Q =[]\n",
        "good_U=[]\n",
        "bad_U=[]\n",
        "bad_Q_print=0.0\n",
        "bad_Q =[]\n",
        "deep_layers = 2\n",
        "#initialize random\n",
        "W = np.random.rand(deep_layers,4,3) * 1.3\n",
        "W_before =  W.copy()\n",
        "#initialize a random replay memory\n",
        "while True:\n",
        "  if count>=N:\n",
        "    break\n",
        "  s_i = random_state()\n",
        "  while True:\n",
        "    action = int(np.random.rand()*4)\n",
        "    s_f = Next_position(s_i,action)\n",
        "    reward = R[s_f]\n",
        "    D.append([s_i,action,reward,s_f])\n",
        "    count = count + 1\n",
        "    if count>=N:\n",
        "      break\n",
        "    if (s_f in ALIVE_indexes) or (s_f in DEATH_indexes):\n",
        "      break\n",
        "    s_i = s_f\n",
        "\n",
        "\n",
        "epsilon=epsilon_0\n",
        "loss_game=0\n",
        "loss_games=[]\n",
        "game_reward=[]\n",
        "games_reward=[]\n",
        "good_diff_V = []\n",
        "bad_diff_V = []\n",
        "cntr=0\n",
        "var_init_circuit = torch.tensor(0.3* np.random.randn(deep_layers,4, 3), device='cpu', requires_grad=True).type(dtype)\n",
        "\n",
        "# Define the two Q value function initial parameters\n",
        "# Use np copy() function to DEEP COPY the numpy array\n",
        "var_Q_circuit = var_init_circuit\n",
        "var_target_Q_circuit = var_Q_circuit.clone().detach()\n",
        "\n",
        "opt = torch.optim.RMSprop([var_Q_circuit],lr=LR_0, alpha=0.99,eps=1e-08,weight_decay=0,momentum=0,centered=False)\n",
        "#test\n",
        "print(\"TEST\")\n",
        "print(var_Q_circuit)\n",
        "print(variational_classifier(var_Q_circuit = var_Q_circuit, angles = decimalToBinaryFixLength(4,9)))\n",
        "print()\n",
        "\n",
        "scheduler = optim.lr_scheduler.StepLR(opt, step_size=3, gamma=0.5)\n",
        "MAE_min = 1000.0\n",
        "\n",
        "print(qml.draw(circuit)(var_Q_circuit, angles = decimalToBinaryFixLength(4,9)))\n",
        "for m in range(num_games):\n",
        "  #choose a random state\n",
        "  s_1 = random_state()\n",
        "  #s_1=0\n",
        "  reward_history=[]\n",
        "  game_reward = 0.0\n",
        "  cntr=0\n",
        "  loss_game=0\n",
        "  for time in range(max_time):\n",
        "    a = epsilon_greedy(var_Q_circuit = var_Q_circuit, epsilon = epsilon, n_action = 4, s = s_1)\n",
        "    s_2 = Next_position(s_1,a)\n",
        "    reward = R[s_2]\n",
        "    D.append([s_1,a,reward,s_2]) #store in the replay memory\n",
        "    s_1 = s_2 #reset the state\n",
        "    if len(D)>=N:\n",
        "      D.pop(0)   #eliminate too old memories\n",
        "\n",
        "    #sample minibatch from D\n",
        "\n",
        "    batch = sample_random_batch(D, len_batch)\n",
        "    loss=0.0\n",
        "\n",
        "    for j in range(len_batch):\n",
        "      s_j, action_j, reward_j, s_jf = batch[j][0],batch[j][1],batch[j][2],batch[j][3]\n",
        "\n",
        "      #print(s_jf, phi_jf)\n",
        "      if (s_jf in ALIVE_indexes) or (s_jf in DEATH_indexes):\n",
        "        y = reward_j+ max(variational_classifier(var_Q_circuit = var_target_Q_circuit, angles = decimalToBinaryFixLength(4,s_jf))) * 0.0\n",
        "        #print(\"ALIVE OR DEATH\")\n",
        "      else:\n",
        "        #print(s_jf)\n",
        "        #print(variational_classifier(var_Q_circuit = var_target_Q_circuit, angles = decimalToBinaryFixLength(4,s_jf)))\n",
        "        y = reward_j + Gamma * max(variational_classifier(var_Q_circuit = var_target_Q_circuit, angles = decimalToBinaryFixLength(4,s_jf)))\n",
        "      #if s_j==10 and action_j ==1:\n",
        "        #print(y,Q(phi_j)[action_j]\n",
        "      pred=variational_classifier(var_Q_circuit = var_Q_circuit, angles = decimalToBinaryFixLength(4,s_jf))[action_j]\n",
        "      #print(pred,pred.type)\n",
        "      loss +=loss_fnc(pred,y) / len_batch\n",
        "    #print(loss.type(), y.type(), pred.type())\n",
        "    #print(pred.grad)\n",
        "\t\t# print(loss)\n",
        "    opt.zero_grad()\n",
        "    #print(type(loss))\n",
        "    loss.backward()\n",
        "\n",
        "    opt.step()\n",
        "\n",
        "    #loss.backward()\n",
        "    #print(loss, pred.grad)\n",
        "    #W = quantum_backprop(W,alpha,pred.grad, s_j, action_j, verbose=True)\n",
        "    #W = quantum_backprop2(W,alpha,batch, verbose=False)\n",
        "    #print(W)\n",
        "    #print()\n",
        "\n",
        "    loss_game+=loss.item()\n",
        "    reward_history.append(reward)\n",
        "    cntr+=1\n",
        "    c+=1\n",
        "    if c>=C:\n",
        "      var_target_Q_circuit = var_Q_circuit.clone().detach()\n",
        "      c=0\n",
        "      #print(\"Update of W_before!\")\n",
        "\n",
        "    if (s_2 in ALIVE_indexes) or (s_2 in DEATH_indexes):\n",
        "      break\n",
        "\n",
        "  for esponent in range(len(reward_history)):\n",
        "    game_reward += reward_history[esponent]*Gamma**esponent\n",
        "  games_reward.append(game_reward)\n",
        "  loss_games.append(loss_game/cntr)\n",
        "  epsilon = np.max([epsilon_0 - (float(m+1)/2000),epsilon_min])\n",
        "  print(cntr)\n",
        "\n",
        "  if opt.param_groups[0]['lr']> LR_MIN:\n",
        "    print(\"Ustep of lr2:\", opt.param_groups[0]['lr'])\n",
        "    scheduler.step()\n",
        "  if m%20==0 and m>0:\n",
        "    good_Q_values = variational_classifier(var_Q_circuit = var_Q_circuit, angles = decimalToBinaryFixLength(4,10))\n",
        "    good_V = torch.max(torch.tensor(good_Q_values)).item()\n",
        "    good_diff_V.append(good_V- U_final[10])\n",
        "    good_Q_print = good_Q_values[1]\n",
        "\n",
        "    bad_Q_values = variational_classifier(var_Q_circuit = var_Q_circuit, angles = decimalToBinaryFixLength(4,3))\n",
        "    bad_V = torch.max(torch.tensor(bad_Q_values)).item()\n",
        "    bad_diff_V.append(bad_V - U_final[3])\n",
        "    bad_Q_print = bad_Q_values[0]\n",
        "    bad_Q2_print = bad_Q_values[1]\n",
        "    MAE_value=0.0\n",
        "    for i in range(12):\n",
        "      if (i not in ALIVE_indexes) and (i not in DEATH_indexes) and (i not in OBSTACLES_indexes):\n",
        "        U_tmp = torch.max(torch.tensor(variational_classifier(var_Q_circuit = var_Q_circuit, angles = decimalToBinaryFixLength(4,i))))\n",
        "        MAE_value+=(U_tmp - U_final[i].item())**2/9.0\n",
        "    MAE_U.append(MAE_value)\n",
        "    good_Q.append( good_Q_print)\n",
        "    bad_Q.append( bad_Q_print)\n",
        "    #alpha = alpha_0-m*(alpha_0-alpha_min)/num_games\n",
        "    print(\"GOOD Q (left to the green):\",good_Q_print.item(), \", BAD Q (under the red)\", bad_Q_print.item(),\", BAD Q 2(left of the red)\", bad_Q2_print.item())\n",
        "    print(\"GOOD V (left to the green):\",good_V, \", BAD V (under the red)\", bad_V)\n",
        "    print(\"MSE_value=\",MAE_value)\n",
        "\n",
        "    print(qml.draw(circuit)(var_Q_circuit, angles = decimalToBinaryFixLength(4,9)))\n",
        "\n",
        "    print(\"Learning rate:\", opt.param_groups[0]['lr'],\"MSE=\",MAE_value)\n",
        "    print(f\"GAME = {m}, LOSSavg20 = {np.mean(loss_games[:-20:]):.8f}, number of steps done in last= {cntr}, last_episode's_last_state = {s_2:.0f}, epsilon = {epsilon:.4f}, game_rewardavg20 = {np.mean(games_reward[:-20:]):.4f}\")\n",
        "    if m>0:\n",
        "      if MAE_value< MAE_min:\n",
        "        best_weights = var_Q_circuit.clone().detach()\n",
        "        MAE_min = MAE_value\n",
        "  if m==0:\n",
        "    print(loss_games[0])\n",
        "  cntr=0\n",
        "  loss_game=0.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (2.3.1)\n",
            "Requirement already satisfied: filelock in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from torch) (3.15.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from torch) (4.10.0)\n",
            "Requirement already satisfied: sympy in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from torch) (1.12)\n",
            "Requirement already satisfied: networkx in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from torch) (3.3)\n",
            "Requirement already satisfied: jinja2 in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from torch) (3.1.4)\n",
            "Requirement already satisfied: fsspec in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from torch) (2024.6.1)\n",
            "Requirement already satisfied: mkl<=2021.4.0,>=2021.1.1 in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from torch) (2021.4.0)\n",
            "Requirement already satisfied: intel-openmp==2021.* in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from mkl<=2021.4.0,>=2021.1.1->torch) (2021.4.0)\n",
            "Requirement already satisfied: tbb==2021.* in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from mkl<=2021.4.0,>=2021.1.1->torch) (2021.13.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from jinja2->torch) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in c:\\users\\andyb\\anaconda3\\envs\\qiskit\\lib\\site-packages (from sympy->torch) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install torch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Analysis ##"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "MomJpsXjllwS"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "179\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#MAE IN REALITY IS MSE\n",
        "x_MSE = [float(i)/len(MAE_U) for i in range(len(MAE_U))]\n",
        "plt.plot(x_MSE,MAE_U, label =\"MSE\", color=\"black\")\n",
        "\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.xlabel(\"epochs of training(relative)\")\n",
        "print(len(MAE_U)) #firstly 720 alpha 0.01"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "4ysWlRxifE6O"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "jump_reward = 20\n",
        "jump_loss = 20\n",
        "games_reward_means = games_reward\n",
        "loss_games_means = loss_games\n",
        "fig, ax1 = plt.subplots()\n",
        "x_reward = [i/len(games_reward_means[::jump_reward]) for i in range(len(games_reward_means[::jump_reward])+1)]\n",
        "\n",
        "x_loss = [i/len(loss_games_means[::jump_loss]) for i in range(len(loss_games_means[::jump_loss])+1)]\n",
        "\n",
        "count =0\n",
        "index=0\n",
        "\n",
        "games_reward_means = np.zeros(len(x_reward))\n",
        "std_reward_games = np.zeros(len(x_reward))\n",
        "for i in range(len(games_reward)):\n",
        "  count+=1\n",
        "  games_reward_means[index]+=games_reward[i]/jump_reward\n",
        "  if count == jump_reward:\n",
        "    index+=1\n",
        "    count=0\n",
        "index=0\n",
        "for j in range(len(games_reward)):\n",
        "  count+=1\n",
        "  std_reward_games[index]+=(games_reward[i]-games_reward_means[index])**2/jump_reward\n",
        "  if count == jump_reward:\n",
        "    std_reward_games[index] = np.sqrt(std_reward_games[index])\n",
        "    index+=1\n",
        "    count=0\n",
        "\n",
        "index=0\n",
        "loss_games_means = np.zeros(len(x_loss))\n",
        "for i in range(len(loss_games)):\n",
        "  count+=1\n",
        "  loss_games_means[index]+=loss_games[i]/jump_loss\n",
        "  if count == jump_loss:\n",
        "    index+=1\n",
        "    count=0\n",
        "# Primo plot\n",
        "color = 'tab:green'\n",
        "ax1.set_xlabel('epochs of training(relative)')\n",
        "ax1.set_ylabel('Asse y1', color=color)\n",
        "ax1.plot(x_reward,games_reward_means,color=\"green\")\n",
        "\n",
        "#ax1.plot(x_reward,games_reward_means + std_reward_games,color=\"green\", alpha=0.2)\n",
        "#ax1.plot(x_reward,games_reward_means - std_reward_games,color=\"green\", alpha=0.2)\n",
        "\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "ax1.set_ylabel(\"Rewards\")\n",
        "\n",
        "\n",
        "# Secondo asse y\n",
        "ax2 = ax1.twinx()\n",
        "color = 'tab:red'\n",
        "ax2.set_ylabel('Asse y2', color=color)\n",
        "ax2.plot(x_loss,loss_games_means, marker=\"\", linestyle =\"-\", color =\"red\", alpha =0.6)\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "ax2.set_yscale(\"log\")\n",
        "ax2.set_ylabel(\"loss (log scale)\")\n",
        "\n",
        "#fig.tight_layout()  # Per aggiustare il layout\n",
        "plt.savefig(\"LOSS_REWARD.pdf\")\n",
        "plt.show()\n",
        "plt.figure()\n",
        "plt.ylabel(\"loss\")\n",
        "plt.xlabel(\"epochs of training(relative)\")\n",
        "plt.plot(x_loss,loss_games_means, marker=\"\", linestyle =\"-\", color =\"red\", alpha =0.6)\n",
        "plt.yscale(\"log\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "akChzni1OgZJ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          "data": {
            "text/plain": [
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(good_Q)\n",
        "plt.plot([i.item() for i in good_Q])\n",
        "\n",
        "plt.plot([i.item() for i in bad_Q], label=\"\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "PnUIELGrfZyy"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\andyb\\anaconda3\\envs\\qiskit\\Lib\\site-packages\\pennylane\\math\\utils.py:228: UserWarning: Contains tensors of types {'torch', 'autograd'}; dispatch will prioritize TensorFlow, PyTorch, and  Jax over Autograd. Consider replacing Autograd with vanilla NumPy.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.4553359724071072\n",
            "0.4553359724071064\n",
            "0.36888591951698846\n",
            "0.3688859195169883\n",
            "0.12644993270332\n",
            "0.08255806193052273\n",
            "0.845716570777465\n",
            "0.845716570777464\n",
            "0.845716570777464\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x1a1b0f65610>]"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def Q(s,W):\n",
        "  return variational_classifier(var_Q_circuit = W, angles = decimalToBinaryFixLength(4,s))\n",
        "\n",
        "policy_DEEP = np.zeros(Nx*Ny)\n",
        "U_DEEP = np.zeros(12)\n",
        "for s in range(Nx*Ny):\n",
        "  #action = torch.argmax(torch.tensor(Q(s,best_weights)))\n",
        "  action = torch.argmax(torch.tensor(Q(s,var_Q_circuit)))\n",
        "  policy_DEEP[s] = action\n",
        "  #U_DEEP[s] = torch.max(torch.tensor(Q(s,best_weights))).item()\n",
        "  U_DEEP[s] = torch.max(torch.tensor(Q(s,var_Q_circuit))).item()\n",
        "\n",
        "U_PLOT_DEEP = []\n",
        "for s in range(Nx*Ny):\n",
        "  if (s not in ALIVE_indexes) and  (s not in DEATH_indexes) and (s not in OBSTACLES_indexes):\n",
        "    print(U_DEEP[s])\n",
        "    U_PLOT_DEEP.append(U_DEEP[s])\n",
        "    \n",
        "index_CORRECT = [0,1,2,3,4,6,8,9,10]\n",
        "\n",
        "plt.plot(index_CORRECT,U_PLOT_DEEP)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "R7MyD3acfk5K"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "my_title=\"Policy\"\n",
        "plot_policy(policy_DEEP, title = my_title)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5OoDhkuk1Qg1"
      },
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "fQ5K8kAPovCK",
        "oOUFDKCJCJ5Z"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Qiskit (stable)",
      "language": "python",
      "name": "qiskit-stable"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}