A3C_KungFuMaster.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dIo6Zkp7U1Hq"
      },
      "source": [
        "# A3C for Kung Fu"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pz8ogVxGVB6b"
      },
      "source": [
        "## Part 0 - Installing the required packages and importing the libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqN2IEX1VKzi"
      },
      "source": [
        "### Installing Gymnasium"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dbnq3XpoKa_7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5c7132d-e886-4aa2-eeb6-d633584fe405"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gymnasium in /usr/local/lib/python3.10/dist-packages (0.29.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium) (1.23.5)\n",
            "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium) (2.2.1)\n",
            "Requirement already satisfied: typing-extensions>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium) (4.5.0)\n",
            "Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from gymnasium) (0.0.4)\n",
            "Requirement already satisfied: gymnasium[accept-rom-license,atari] in /usr/local/lib/python3.10/dist-packages (0.29.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (1.23.5)\n",
            "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (2.2.1)\n",
            "Requirement already satisfied: typing-extensions>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (4.5.0)\n",
            "Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (0.0.4)\n",
            "Requirement already satisfied: autorom[accept-rom-license]~=0.4.2 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (0.4.2)\n",
            "Requirement already satisfied: shimmy[atari]<1.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[accept-rom-license,atari]) (0.2.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (8.1.7)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (4.66.1)\n",
            "Requirement already satisfied: AutoROM.accept-rom-license in /usr/local/lib/python3.10/dist-packages (from autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (0.6.1)\n",
            "Requirement already satisfied: ale-py~=0.8.1 in /usr/local/lib/python3.10/dist-packages (from shimmy[atari]<1.0,>=0.1.0->gymnasium[accept-rom-license,atari]) (0.8.1)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.10/dist-packages (from ale-py~=0.8.1->shimmy[atari]<1.0,>=0.1.0->gymnasium[accept-rom-license,atari]) (6.1.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->gymnasium[accept-rom-license,atari]) (2023.11.17)\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "swig is already the newest version (4.0.2-1ubuntu1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 15 not upgraded.\n",
            "Requirement already satisfied: gymnasium[box2d] in /usr/local/lib/python3.10/dist-packages (0.29.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (1.23.5)\n",
            "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (2.2.1)\n",
            "Requirement already satisfied: typing-extensions>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (4.5.0)\n",
            "Requirement already satisfied: farama-notifications>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (0.0.4)\n",
            "Requirement already satisfied: box2d-py==2.3.5 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (2.3.5)\n",
            "Requirement already satisfied: pygame>=2.1.3 in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (2.5.2)\n",
            "Requirement already satisfied: swig==4.* in /usr/local/lib/python3.10/dist-packages (from gymnasium[box2d]) (4.1.1.post1)\n"
          ]
        }
      ],
      "source": [
        "!pip install gymnasium\n",
        "!pip install \"gymnasium[atari, accept-rom-license]\"\n",
        "!apt-get install -y swig\n",
        "!pip install gymnasium[box2d]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BrsNHNQqVZLK"
      },
      "source": [
        "### Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ho_25-9_9qnu"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import math\n",
        "import random\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import torch.multiprocessing as mp\n",
        "import torch.distributions as distributions\n",
        "from torch.distributions import Categorical\n",
        "import gymnasium as gym\n",
        "from gymnasium import ObservationWrapper\n",
        "from gymnasium.spaces import Box"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VF6EFSGUVlk2"
      },
      "source": [
        "## Part 1 - Building the AI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qyNc8cxbZCYP"
      },
      "source": [
        "### Creating the architecture of the Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eYFXu6EAT4Kw"
      },
      "outputs": [],
      "source": [
        "class Network(nn.Module):\n",
        "\n",
        "  def __init__(self, action_size): #Input action_size only for Convolutional Neural Networks as in DeepQconv.\n",
        "    super(Network, self).__init__() #Network inherits from nn.Module super activates the inheretance\n",
        "    self.conv1 = torch.nn.Conv2d(in_channels = 4,  out_channels = 32, kernel_size = (3,3), stride = 2) #intitlized with 4 greyscale frame for KungfuEnvironment\n",
        "    self.conv2 = torch.nn.Conv2d(in_channels = 32, out_channels = 32, kernel_size = (3,3), stride = 2) #These values are selected for optimal tuning\n",
        "    self.conv3 = torch.nn.Conv2d(in_channels = 32, out_channels = 32, kernel_size = (3,3), stride = 2)\n",
        "    self.flatten = torch.nn.Flatten() # Creating Flatening Layer\n",
        "    self.fc1  = torch.nn.Linear(512, 128)\n",
        "    self.fc2a = torch.nn.Linear(128, action_size) # Final Output Layer 1 -- Q values\n",
        "    self.fc2s = torch.nn.Linear(128, 1) # Final Output Layer 2 -- Critic\n",
        "\n",
        "  def forward(self, state): # state as in input frames\n",
        "    x = self.conv1(state)\n",
        "    x = F.relu(x)\n",
        "    x = self.conv2(x)\n",
        "    x = F.relu(x)\n",
        "    x = self.conv3(x)\n",
        "    x = F.relu(x)\n",
        "    x = self.flatten(x)\n",
        "    x = self.fc1(x)\n",
        "    x = F.relu(x)\n",
        "    action_values = self.fc2a(x) # Q values that the AI can play\n",
        "    state_value = self.fc2s(x)[0] # State value V(s) (0) just gives the value not in any vector or array.\n",
        "    return action_values, state_value"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eF5bETqbZbCG"
      },
      "source": [
        "## Part 2 - Training the AI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3C2ydyKLZgaK"
      },
      "source": [
        "### Setting up the environment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gF756uIhRVcK",
        "outputId": "df7c9b69-75d3-4ff9-de34-3d2f899cea2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "State shape: (4, 42, 42)\n",
            "Number actions: 14\n",
            "Action names: ['NOOP', 'UP', 'RIGHT', 'LEFT', 'DOWN', 'DOWNRIGHT', 'DOWNLEFT', 'RIGHTFIRE', 'LEFTFIRE', 'DOWNFIRE', 'UPRIGHTFIRE', 'UPLEFTFIRE', 'DOWNRIGHTFIRE', 'DOWNLEFTFIRE']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.get_action_meanings to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.get_action_meanings` for environment variables or `env.get_wrapper_attr('get_action_meanings')` that will search the reminding wrappers.\u001b[0m\n",
            "  logger.warn(\n"
          ]
        }
      ],
      "source": [
        "class PreprocessAtari(ObservationWrapper): #Preprocessed Atari Class\n",
        "\n",
        "  def __init__(self, env, height = 42, width = 42, crop = lambda img: img, dim_order = 'pytorch', color = False, n_frames = 4): #Defines Properties of the Environment\n",
        "    super(PreprocessAtari, self).__init__(env)\n",
        "    self.img_size = (height, width)\n",
        "    self.crop = crop\n",
        "    self.dim_order = dim_order\n",
        "    self.color = color\n",
        "    self.frame_stack = n_frames\n",
        "    n_channels = 3 * n_frames if color else n_frames\n",
        "    obs_shape = {'tensorflow': (height, width, n_channels), 'pytorch': (n_channels, height, width)}[dim_order]\n",
        "    self.observation_space = Box(0.0, 1.0, obs_shape)\n",
        "    self.frames = np.zeros(obs_shape, dtype = np.float32)\n",
        "\n",
        "  def reset(self): #Metod to Reset the environment\n",
        "    self.frames = np.zeros_like(self.frames)\n",
        "    obs, info = self.env.reset()\n",
        "    self.update_buffer(obs)\n",
        "    return self.frames, info\n",
        "\n",
        "  def observation(self, img): #Preprocess Images of the environment\n",
        "    img = self.crop(img)\n",
        "    img = cv2.resize(img, self.img_size)\n",
        "    if not self.color:\n",
        "      if len(img.shape) == 3 and img.shape[2] == 3:\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "    img = img.astype('float32') / 255.\n",
        "    if self.color:\n",
        "      self.frames = np.roll(self.frames, shift = -3, axis = 0)\n",
        "    else:\n",
        "      self.frames = np.roll(self.frames, shift = -1, axis = 0)\n",
        "    if self.color:\n",
        "      self.frames[-3:] = img\n",
        "    else:\n",
        "      self.frames[-1] = img\n",
        "    return self.frames\n",
        "\n",
        "  def update_buffer(self, obs): #Updates the buffer\n",
        "    self.frames = self.observation(obs)\n",
        "\n",
        "def make_env(): #Creating the environment variable using Preprocessed Atariclass\n",
        "  env = gym.make(\"KungFuMasterDeterministic-v0\", render_mode = 'rgb_array')\n",
        "  env = PreprocessAtari(env, height = 42, width = 42, crop = lambda img: img, dim_order = 'pytorch', color = False, n_frames = 4)\n",
        "  return env\n",
        "\n",
        "env = make_env() #Created Environment\n",
        "\n",
        "state_shape = env.observation_space.shape\n",
        "number_actions = env.action_space.n\n",
        "print(\"State shape:\", state_shape) # 4 greyscale images of dimensions 42x42\n",
        "print(\"Number actions:\", number_actions) # 14 possible actions AI can play\n",
        "print(\"Action names:\", env.env.env.get_action_meanings()) #Prints action names in the environment."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YgRlooBmC1hr"
      },
      "source": [
        "### Initializing the hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iio4XgZhC2XN"
      },
      "outputs": [],
      "source": [
        "learning_rate = 1e-4 #Of Adam\n",
        "discount_factor = 0.99\n",
        "number_environments = 10 # To Train Multiple Agents in Multiple Environments in Parallel. Conversion will be very fast"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gg_LmSs9IoTX"
      },
      "source": [
        "### Implementing the A3C class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uNvmCJIDIwVZ"
      },
      "outputs": [],
      "source": [
        "class Agent():\n",
        "\n",
        "  def __init__(self, action_size):\n",
        "    self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "    self.action_size = action_size\n",
        "    self.network = Network(action_size).to(self.device) #only one network\n",
        "    self.optimizer = torch.optim.Adam(self.network.parameters(), lr = learning_rate)\n",
        "\n",
        "  def act(self, state):\n",
        "    if state.ndim == 3: # In 3 dimensions 1 for which frame and 42x42 are 2  4 greyscales images here , if it is not in a batch\n",
        "      state = [state] #extra dimsnsion 4 ie batch\n",
        "    state = torch.tensor(state, dtype = torch.float32, device = self.device) #converting to a torch tensor.\n",
        "    action_values, _ = self.network(state) #Callinng network to get the action Values, self.network.foreward(state) is what we are calling which is understood by the pytorch by default\n",
        "    policy = F.softmax(action_values, dim = -1) #Softmax Policy insted in epsilon greedy because it is better here. -1 incicates last dimension\n",
        "    return np.array([np.random.choice(len(p), p = p) for p in policy.detach().cpu().numpy()]) #Several actions to be returned in a numpy array choice allows to select from each probablity distribution (length(p),policy) detach --> detaches policy tensor from computational graph\n",
        "\n",
        "  def step(self, state, action, reward, next_state, done): #updates model parameters after taking a step\n",
        "    batch_size = state.shape[0] #First index used as a extra dimension\n",
        "    state = torch.tensor(state, dtype = torch.float32, device = self.device) #Converting all to torch tensors\n",
        "    next_state = torch.tensor(next_state, dtype = torch.float32, device = self.device)\n",
        "    reward = torch.tensor(reward, dtype = torch.float32, device = self.device)\n",
        "    done = torch.tensor(done, dtype = torch.bool, device = self.device).to(dtype = torch.float32) # dtype float 32 to change it to float again because all computations are in float.\n",
        "    action_values, state_value = self.network(state) # returns the q value and critic\n",
        "    _, next_state_value = self.network(next_state) # returning net state vlaue (critic)\n",
        "    target_state_value = reward + discount_factor * next_state_value * (1 - done) #Bellman equation\n",
        "    advantage = target_state_value - state_value # Advantage of A3C (Part of Actor's Loss) Here we will compute two losses\n",
        "    probs = F.softmax(action_values, dim = -1) #Entropy for Actors Loss Probablities over action values and log probablities over same action values\n",
        "    logprobs = F.log_softmax(action_values, dim = -1)\n",
        "    entropy = -torch.sum(probs * logprobs, axis = -1) #Entropy -1 Represnts Last dimension\n",
        "    batch_idx = np.arange(batch_size) #Selecting log probablities of the actions that are taken in the batch.\n",
        "    logp_actions = logprobs[batch_idx, action] #Selection of only the indexes that are selected\n",
        "    actor_loss = -(logp_actions * advantage.detach()).mean() - 0.001 * entropy.mean()  # -ve mean of the product of log probablities of the actions played and the advantages - 0.001*entropy(exploration feature) which balances importance of entropy\n",
        "    critic_loss = F.mse_loss(target_state_value.detach(), state_value) # MSE btw target state value and the state value\n",
        "    total_loss = actor_loss + critic_loss\n",
        "    self.optimizer.zero_grad() #Reseting Optimizer zeroing out gradients\n",
        "    total_loss.backward() #BackPropogation\n",
        "    self.optimizer.step() #Step from Adam which updates the neural networks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7RnRukHDKFJ0"
      },
      "source": [
        "### Initializing the A3C agent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_V6RUlHdKFSz"
      },
      "outputs": [],
      "source": [
        "agent = Agent(number_actions)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oB5SpmoKP0aK"
      },
      "source": [
        "### Evaluating our A3C agent on a certain number of episodes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RU1v_9UbUQv7"
      },
      "outputs": [],
      "source": [
        "def evaluate(agent, env, n_episodes = 1): #Evaluates A3C on certain number of episodes\n",
        "  episodes_rewards = [] #list of episode rewards\n",
        "  for _ in range(n_episodes): #looping on number of episodes\n",
        "    state, _ = env.reset() # resetting environment (initilized state) returns state and some variables which are discarded\n",
        "    total_reward = 0\n",
        "    while True:\n",
        "      action = agent.act(state) # Playing a action in first time step\n",
        "      state, reward, done, info, _ = env.step(action[0]) #step metod which calcaulates all these parameters info is not requiered but taken and one more value discarded aciton[0] beacuse it is a numpy array.\n",
        "      total_reward += reward\n",
        "      if done:\n",
        "        break\n",
        "    episodes_rewards.append(total_reward) # Appending rewards of each episodes.\n",
        "  return episodes_rewards # Returns rewards in each episode (list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVSqiyjiQeMd"
      },
      "source": [
        "### Managing multiple environments simultaneously"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1GPEjnFyU_GD"
      },
      "outputs": [],
      "source": [
        "# Asynchronous\n",
        "class EnvBatch: #Handeling multiple enviornments (Parallelized Reinforcemnet Learning)\n",
        "\n",
        "  def __init__(self, n_envs = 10): # By default Creating 10 environments.\n",
        "    self.envs = [make_env() for _ in range(n_envs)] # Making environments using make_env() function with for loop in one shot. (0 to 9 environments)\n",
        "\n",
        "  def reset(self): # Resets multiple enviroments at the same time.\n",
        "    _states = [] # new list of initilized states from self.envs object containing differnent environments.\n",
        "    for env in self.envs:\n",
        "      _states.append(env.reset()[0]) #(0) index that stores only states wand returns.\n",
        "    return np.array(_states)\n",
        "\n",
        "  def step(self, actions): # Allows to step in multiple environments simultaneously. Inputs actions by different agents.\n",
        "    next_states, rewards, dones, infos, _ = map(np.array, zip(*[env.step(a) for env, a in zip(self.envs, actions)])) # calling stepmethod from multiple environments which returns all these parameters\n",
        "    # 2 loops runnig here env.step(a) -> step in each environment and for env (each environment) , a (each action in actions) zip(self.env() , actions) takes looping from two varible simultaneously ie self.env and actions which are arrays\n",
        "    # a zip * allows to group all the env variables next_states, rewards etc.\n",
        "    # map function converts all the  elements to numpy arrays. to zip of grouped elements.\n",
        "    for i in range(len(self.envs)): # for all environments.\n",
        "      if dones[i]: # If a perticular environment si done then reset the environment last step berfore returning elements.\n",
        "        next_states[i] = self.envs[i].reset()[0]\n",
        "    return next_states, rewards, dones, infos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69WZWB4oRx1P"
      },
      "source": [
        "### Training the A3C agent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HFkkVttVxSM8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32070b2e-bef7-48bc-c2b2-d404015e371e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/4001 [00:00<?, ?it/s]<ipython-input-23-755aac6bbe4e>:33: UserWarning: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([10])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
            "  critic_loss = F.mse_loss(target_state_value.detach(), state_value) # MSE btw target state value and the state value\n",
            "  0%|          | 5/4001 [00:41<6:53:33,  6.21s/it] "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average agent reward:  440.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 25%|██▌       | 1008/4001 [01:41<1:02:16,  1.25s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average agent reward:  570.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 50%|█████     | 2008/4001 [02:40<58:36,  1.76s/it]  "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average agent reward:  730.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 75%|███████▌  | 3008/4001 [03:35<26:40,  1.61s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average agent reward:  550.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4001/4001 [04:27<00:00, 14.93it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average agent reward:  500.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "import tqdm # Displays progress bar for loops\n",
        "\n",
        "env_batch = EnvBatch(number_environments) # Creating 10 environments\n",
        "batch_states = env_batch.reset() #Reset all states in every environments\n",
        "\n",
        "with tqdm.trange(0, 3001) as progress_bar: #tqdm shows progress bar and prints the average score of the batch\n",
        "  for i in progress_bar: # as progress bar sets the range as 3000.\n",
        "    batch_actions = agent.act(batch_states) # all actions\n",
        "    batch_next_states, batch_rewards, batch_dones, _ = env_batch.step(batch_actions) #all next states , rewards , dones calling multi environment step.\n",
        "    batch_rewards *= 0.01 #Reduce the batch rewards to stabalize the training (we still need to optimize reduced rewards, which will not affect training)\n",
        "    agent.step(batch_states, batch_actions, batch_rewards, batch_next_states, batch_dones)# coputes active loss and critic loss and combines these losses together and backpropogating the total loss in the NN with Adam optimizer that will update the parameters. For all batches.\n",
        "    batch_states = batch_next_states #Update the batch states.\n",
        "    if i % 1000 == 0: # Printing every 1000 iterations.\n",
        "      print(\"Average agent reward: \", np.mean(evaluate(agent, env, n_episodes = 10))) # Mean of output of the evaluate Function."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7kG_YR9YdmUM"
      },
      "source": [
        "## Part 3 - Visualizing the results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "UGkTuO6DxZ6B",
        "outputId": "9f9d40ed-c186-4388-be6d-679236bb534d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:imageio_ffmpeg:IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (160, 210) to (160, 224) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<video alt=\"test\" autoplay\n",
              "                loop controls style=\"height: 400px;\">\n",
              "                <source src=\"data:video/mp4;base64,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\" type=\"video/mp4\" />\n",
              "             </video>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import glob\n",
        "import io\n",
        "import base64\n",
        "import imageio\n",
        "from IPython.display import HTML, display\n",
        "from gymnasium.wrappers.monitoring.video_recorder import VideoRecorder\n",
        "\n",
        "def show_video_of_model(agent, env):\n",
        "  state, _ = env.reset()\n",
        "  done = False\n",
        "  frames = []\n",
        "  while not done:\n",
        "    frame = env.render()\n",
        "    frames.append(frame)\n",
        "    action = agent.act(state)\n",
        "    state, reward, done, _, _ = env.step(action[0])\n",
        "  env.close()\n",
        "  imageio.mimsave('video.mp4', frames, fps=30)\n",
        "\n",
        "show_video_of_model(agent, env)\n",
        "\n",
        "def show_video():\n",
        "    mp4list = glob.glob('*.mp4')\n",
        "    if len(mp4list) > 0:\n",
        "        mp4 = mp4list[0]\n",
        "        video = io.open(mp4, 'r+b').read()\n",
        "        encoded = base64.b64encode(video)\n",
        "        display(HTML(data='''<video alt=\"test\" autoplay\n",
        "                loop controls style=\"height: 400px;\">\n",
        "                <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" />\n",
        "             </video>'''.format(encoded.decode('ascii'))))\n",
        "    else:\n",
        "        print(\"Could not find video\")\n",
        "\n",
        "show_video()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}