The k-Αrmed Bandit Problem.ipynb

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   "source": [
    "# The k-Armed Bandit Problem\n",
    "## An introduction to Reinforcement Learning"
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   "source": [
    "### Fred's Night\n",
    "After a week of hard work, Fred decides to spend his Saturday evening in a local bar. He orders a beer and while drinking his beverage, takes a glance at the slot machines deep inside the room. Fascinated by their bleeping sounds and flashing colours, he decides to take a shot at the slots. He has never played a slot machine before and he had some spare money to gamble, so he though: \"why not?\". So, he takes his beer and walks to the slots..."
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    "<img src=\"img/slot_machines.jpg\" margin=\"\">"
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    "### Playing Slots\n",
    "<p>There were three slot machines in a row. No other person was playing. Fred chose the left one, inserted a coin and pulled the handle. He played for a while on this machine, earning some money. Then, he started losing. After he lost his earnings and some of his initial money, he decided to try another slot.</p>\n",
    "<p>Fred tried his luck on the middle one. Apparently, this was a very good decision, because from the first few pulls he earned back his losses and his profit started to increase... fast. Not a single thought of returning to that first slot! After, many rounds he ended up with a good amount of earnings. Then he remembered that he hasn't tried the leftmost slot yet.</p>\n",
    "<p>Wondering if the final slot would earn him more money, Fred started playing with it. He ended up losing all his earnings again and much of his initial money as well. \"This slot was even worse than the first one!\", he thought. Not willing to go home with such a loss, he decided to play the machine in the middle again and pulled the lever.</p>\n",
    "<p>Finally, after balancing back to his initial money and winning some small earnings, Fred watches the clock and decides it's time to go home.</p>"
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    "### Basic concepts of Reinforcement Learning\n",
    "<p>Reinforcement Learning problems have to do with learning how to act in an environment so as to maximise a specific reward. This learning happens by interacting with the environment and without having prior knowledge about it</p>\n",
    "<p>There is an <b>agent</b>, who tries to maximise his <b>rewards</b>/earnings. There is an <b>environment</b>, consisting of multiple <b>states</b>, in which the agent acts. The agent has <b>no knowledge</b> (sometimes maybe a little) on this environment. Thus, it doesn't know about the reward or the punishment that each state of the environment hides.</p>\n",
    "<p>The agent depending on the state it is, decides and takes <b>actions</b> that change the state of the environment. By <b>trial and error</b> it <b>evaluates</b> how beneficial each action is and by these evaluations tries to achieve it's goal. On every state, the agent may have varying actions to decide from, so how does one decide what action to choose next?</p>\n",
    "<p>One agent may go for immediate rewards. Another agent may explore many states before deciding a certain way of acting. The way the agent decides what actions to take next is called the <b>policy</b> of the agent. The goal is to find the optimal policy that maximizes the cumulative reward.</p>"
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   "source": [
    "### Agent Fred\n",
    "<p></p>"
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   "source": [
    "#### Note:\n",
    "<p>\n",
    "In order to be able to see the plots, its is suggested to read the notebook from <a href=\"https://nbviewer.jupyter.org/github/FFF-Fox/k_armed_bandit/blob/master/The%20k-%CE%91rmed%20Bandit%20Problem.ipynb#The-k-Armed-Bandit-Problem\"> nbviewer </a>.\n",
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
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   "source": [
    "import sys\n",
    "sys.path.append('./kArmBandits')\n",
    "from kArmBandits.agent import Agent\n",
    "from kArmBandits.progress import print_progress\n",
    "from kArmBandits.plotting import plot_lines"
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"f20703a2-392a-498c-8ab0-7995d986a0d3\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"f20703a2-392a-498c-8ab0-7995d986a0d3\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'f20703a2-392a-498c-8ab0-7995d986a0d3' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.10.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.10.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.10.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"f20703a2-392a-498c-8ab0-7995d986a0d3\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.io import output_notebook\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# steps  : The number of steps that each sample will act.\n",
    "# N      : The number of samples for each agent.\n",
    "# k      : The number of actions available on each step.\n",
    "# q      : The real value of each action that is unknown to the agent.\n",
    "# agents : A list containing the agents we'd like to sample from.\n",
    "steps = 1500\n",
    "N = 200\n",
    "k = 10\n",
    "q = np.random.normal(0, 1, k)\n",
    "\n",
    "agents = [\n",
    "    Agent(k, q=q, policy_info=[\"e_greedy\", 0.0]),\n",
    "    Agent(k, q=q, policy_info=[\"e_greedy\", 0.1]),\n",
    "    Agent(k, q=q, policy_info=[\"e_greedy\", 0.01])\n",
    "]\n",
    "\n",
    "labels = [\"ε = \"+str(i) for i in [0, 0.1, 0.01]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agent 0 |██████████████████████████████████████████████████| 100.0% \n",
      "Agent 1 |██████████████████████████████████████████████████| 100.0% \n",
      "Agent 2 |██████████████████████████████████████████████████| 100.0% \n"
     ]
    }
   ],
   "source": [
    "# For each sample, we store the actions and rewards of its session.\n",
    "# Then we use that information to find the average reward and the\n",
    "# percentage of optimal actions taken by the samples of the current\n",
    "# agent.\n",
    "avg_rew = []\n",
    "opt_act = []\n",
    "avg_total_rew = []\n",
    "\n",
    "for i, agent in enumerate(agents):\n",
    "    rewards = np.empty([N, steps])\n",
    "    actions = np.empty([N, steps])\n",
    "    total_rewards = np.empty([N, steps])\n",
    "\n",
    "    # We sample the agent N times.\n",
    "    for j in range(N):\n",
    "        metrics = agent.run_session(steps)\n",
    "        \n",
    "        rewards[j] = metrics[\"R\"]\n",
    "        A = metrics[\"A\"]\n",
    "        actions[j] = A == np.argmax(agent.q)\n",
    "        total_rewards[j] = metrics[\"total_reward\"]\n",
    "\n",
    "        print_progress(j+1, N, bar_length=50, prefix=\"Agent \"+str(i))\n",
    "\n",
    "    # Let's calculate the metrics presented above.\n",
    "    avg_rew.append(sum(rewards) / N)\n",
    "    opt_act.append(sum(actions) * 100 / N)\n",
    "    avg_total_rew.append(sum(total_rewards) / N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"f85b0f55-b7d7-41b9-9395-1bc15ee7aab9\"></div>\n",
       "</div>"
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     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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0.1\"},\"renderers\":[{\"id\":\"8806d437-f928-4ecd-9f52-6cbbc87d05b6\",\"type\":\"GlyphRenderer\"}]},\"id\":\"ba666e45-4732-40a0-84bc-f5aa719a7f93\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"5ca1c1e8-6915-4524-8855-bdd062a963d9\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"f15d949c-669b-46db-aba6-a2dae8c64b5f\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"10648a13-2049-4d32-acb6-1d61a0b8e44a\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"993a3f42-a63b-4535-8c1b-a51486464413\",\"type\":\"CDSView\"}},\"id\":\"8806d437-f928-4ecd-9f52-6cbbc87d05b6\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"e6c2be7f-616a-4441-a2b0-daa996ea2b6e\",\"type\":\"PanTool\"},{\"id\":\"f0dd3b4f-c885-41c7-8a67-65e416f79594\",\"type\":\"WheelZoomTool\"},{\"id\":\"8e270c56-0c9b-4abe-acc8-a79cc38f4ff9\",\"type\":\"BoxZoomTool\"},{\"id\":\"f3facf71-f021-476d-aff8-b0410dd80c28\",\"type\":\"SaveTool\"},{\"id\":\"2255d328-74a0-4b43-9c7f-032e50c1dabb\",\"type\":\"ResetTool\"},{\"id\":\"f7c975bd-0bcc-429e-ae63-ed160263b294\",\"type\":\"HelpTool\"}]},\"id\":\"9e777039-1b13-4615-a637-41ee2b316fab\",\"type\":\"Toolbar\"},{\"attributes\":{\"callback\":null},\"id\":\"ed38fed3-4a28-4be1-8e1c-b705d30bbad0\",\"type\":\"DataRange1d\"},{\"attributes\":{\"source\":{\"id\":\"5ca1c1e8-6915-4524-8855-bdd062a963d9\",\"type\":\"ColumnDataSource\"}},\"id\":\"993a3f42-a63b-4535-8c1b-a51486464413\",\"type\":\"CDSView\"},{\"attributes\":{\"line_color\":{\"value\":\"#fdae61\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"09e1d52e-be2f-4812-b3be-d67a516b2eda\",\"type\":\"Line\"},{\"attributes\":{\"label\":{\"value\":\"\\u03b5 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0.01\"},\"renderers\":[{\"id\":\"b2519652-8625-4999-a267-4c57f1fb78f1\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1125a141-2c53-4769-83d9-210c401c2b0b\",\"type\":\"LegendItem\"},{\"attributes\":{\"background_fill_alpha\":{\"value\":0.5},\"background_fill_color\":{\"value\":\"beige\"},\"below\":[{\"id\":\"65fe40ec-1025-49b0-8273-460e0b9920a3\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"24b1061f-6f4d-4aae-815b-8edfe4cf9106\",\"type\":\"LinearAxis\"}],\"plot_height\":400,\"plot_width\":650,\"renderers\":[{\"id\":\"65fe40ec-1025-49b0-8273-460e0b9920a3\",\"type\":\"LinearAxis\"},{\"id\":\"e7f30429-ae65-49b0-a535-89fb00c3bc8d\",\"type\":\"Grid\"},{\"id\":\"24b1061f-6f4d-4aae-815b-8edfe4cf9106\",\"type\":\"LinearAxis\"},{\"id\":\"bad564a2-9741-4b49-9efc-84fa63335fbc\",\"type\":\"Grid\"},{\"id\":\"1f6af710-e924-47e9-991c-167a3f2fd50e\",\"type\":\"BoxAnnotation\"},{\"id\":\"3d772501-fa6c-4999-9a11-dc62854e0050\",\"type\":\"Legend\"},{\"id\":\"9d1843d3-a646-4ffe-93ba-ff8426ff33a2\",\"type\":\"GlyphRenderer\"},{\"id\":\"8806d437-f928-4ecd-9f52-6cbbc87d05b6\",\"type\":\"GlyphRenderer\"},{\"id\":\"b2519652-8625-4999-a267-4c57f1fb78f1\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"754f9f90-29f7-4e81-8b8c-61b047f48653\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"9e777039-1b13-4615-a637-41ee2b316fab\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"ed38fed3-4a28-4be1-8e1c-b705d30bbad0\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"17feb774-938e-4587-8011-77073701752a\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"14404d4d-8d96-4212-b1bc-788fb9e04c17\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"090d81b1-67d3-4168-bdba-fb3ad05f7a8a\",\"type\":\"LinearScale\"}},\"id\":\"3e321638-6d08-4296-b5e1-a3ac7f045d3c\",\"subtype\":\"Figure\",\"type\":\"Plot\"}],\"root_ids\":[\"3e321638-6d08-4296-b5e1-a3ac7f045d3c\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.10\"}};\n",
       "    var render_items = [{\"docid\":\"4ac2ec10-39c8-48bb-a002-7c951bec25e1\",\"elementid\":\"f85b0f55-b7d7-41b9-9395-1bc15ee7aab9\",\"modelid\":\"3e321638-6d08-4296-b5e1-a3ac7f045d3c\"}];\n",
       "\n",
       "    root.Bokeh.embed.embed_items(docs_json, render_items);\n",
       "  }\n",
       "\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to embed document because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "3e321638-6d08-4296-b5e1-a3ac7f045d3c"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lines(avg_rew, labels=labels,\n",
    "           title=\"Reward per step\",\n",
    "           x_label=\"Step\",\n",
    "           y_label=\"Average Reward\"\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"e547cc0d-157c-4cfc-a9ad-9cbfb759d09e\"></div>\n",
       "</div>"
      ]
     },
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= 0\"},\"renderers\":[{\"id\":\"65ee8576-8221-487d-9f47-21cd10a33f5c\",\"type\":\"GlyphRenderer\"}]},\"id\":\"3bba8b15-2ddd-4089-a993-e86c16bacd23\",\"type\":\"LegendItem\"}],\"root_ids\":[\"44658fc3-f4d0-4951-b49b-5f2a15051bac\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.10\"}};\n",
       "    var render_items = [{\"docid\":\"42724b51-dda6-45ab-9f70-217cb7a4df49\",\"elementid\":\"e547cc0d-157c-4cfc-a9ad-9cbfb759d09e\",\"modelid\":\"44658fc3-f4d0-4951-b49b-5f2a15051bac\"}];\n",
       "\n",
       "    root.Bokeh.embed.embed_items(docs_json, render_items);\n",
       "  }\n",
       "\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to embed document because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "44658fc3-f4d0-4951-b49b-5f2a15051bac"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lines(opt_act, labels=labels,\n",
    "           title=\"Percentage of optimal action chosen\",\n",
    "           x_label=\"Step\",\n",
    "           y_label=\"Optimal action chosen %\"\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"e257ce92-e09f-4b3a-b3dc-01e41460cf30\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
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\",\"dtype\":\"float64\",\"shape\":[1500]}}},\"id\":\"e3336fea-d638-4edc-8487-5ca2508597fd\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"7c377efa-26cc-4225-9ee2-f6c893d77042\",\"type\":\"Line\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"32b77f14-8021-46dd-941a-8bf2e5c45e68\",\"type\":\"Line\"},{\"attributes\":{\"label\":{\"value\":\"\\u03b5 = 0\"},\"renderers\":[{\"id\":\"2c75ff71-68e6-4d7a-9d93-ea8b9247a8bc\",\"type\":\"GlyphRenderer\"}]},\"id\":\"9856b4b4-293b-46d8-833c-f6157da77c46\",\"type\":\"LegendItem\"},{\"attributes\":{\"data_source\":{\"id\":\"4731f48a-57f0-458a-962c-bbc2a69b4051\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"8167eb5d-1ed3-42df-9813-2949ad50bd35\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"32b77f14-8021-46dd-941a-8bf2e5c45e68\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"497af78e-4921-4247-b9c1-d89f3df574cc\",\"type\":\"CDSView\"}},\"id\":\"9b0f8e66-2436-471b-84b3-885ea4b43c10\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"label\":{\"value\":\"\\u03b5 = 0.1\"},\"renderers\":[{\"id\":\"9b0f8e66-2436-471b-84b3-885ea4b43c10\",\"type\":\"GlyphRenderer\"}]},\"id\":\"6aa06905-d185-4604-b7f9-0af1a9e1739d\",\"type\":\"LegendItem\"},{\"attributes\":{\"line_color\":{\"value\":\"#abdda4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"8167eb5d-1ed3-42df-9813-2949ad50bd35\",\"type\":\"Line\"},{\"attributes\":{\"source\":{\"id\":\"e3336fea-d638-4edc-8487-5ca2508597fd\",\"type\":\"ColumnDataSource\"}},\"id\":\"5fb01804-ae17-49bc-8657-ba3e1f6019eb\",\"type\":\"CDSView\"}],\"root_ids\":[\"34a66f79-b017-479e-8904-2d8f17cfd06f\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.10\"}};\n",
       "    var render_items = [{\"docid\":\"bccbc401-d5d4-4766-84f5-a451aa654fa9\",\"elementid\":\"e257ce92-e09f-4b3a-b3dc-01e41460cf30\",\"modelid\":\"34a66f79-b017-479e-8904-2d8f17cfd06f\"}];\n",
       "\n",
       "    root.Bokeh.embed.embed_items(docs_json, render_items);\n",
       "  }\n",
       "\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to embed document because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "34a66f79-b017-479e-8904-2d8f17cfd06f"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lines(avg_total_rew, labels=labels,\n",
    "           title=\"Total reward accumulated per step\",\n",
    "           x_label=\"Step\",\n",
    "           y_label=\"Total reward\"\n",
    "          )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}