qlearning.ipynb

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  "metadata": {
    "colab": {
      "name": "qlearning.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ajit2704/Reinforcement-Learning-on-google-colab/blob/master/qlearning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "-80H0xkVljy4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Environment Setup\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "2ajnkaDPlk5l",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install gym"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NS43Lpkl4V7L",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!apt-get install python-opengl -y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dU1M9TEL4bvG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!apt install xvfb -y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NjTFrKSk4lIN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install pyvirtualdisplay\n",
        "!pip install piglet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0fbZNTX04t1i",
        "colab_type": "code",
        "outputId": "6ecd1a79-5046-43d7-e87d-f364b8c9eaad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "from pyvirtualdisplay import Display\n",
        "display = Display(visible=0, size=(1400, 900))\n",
        "display.start()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Display cmd_param=['Xvfb', '-br', '-nolisten', 'tcp', '-screen', '0', '1400x900x24', ':1001'] cmd=['Xvfb', '-br', '-nolisten', 'tcp', '-screen', '0', '1400x900x24', ':1001'] oserror=None return_code=None stdout=\"None\" stderr=\"None\" timeout_happened=False>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "1XfeclNYj4Bt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "cgqTGn8o40rC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "if type(os.environ.get(\"DISPLAY\")) is not str or len(os.environ.get(\"DISPLAY\"))==0:\n",
        "    !bash ../xvfb start\n",
        "    %env DISPLAY=:1\n",
        "        \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "%load_ext autoreload\n",
        "%autoreload 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XMXmidHXlZ4s",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Q-Learning Agent Implementation \n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "Jm812oPv46rq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from collections import defaultdict\n",
        "import random, math\n",
        "import numpy as np\n",
        "\n",
        "class QLearningAgent:\n",
        "    def __init__(self, alpha, epsilon, discount, get_legal_actions):\n",
        "        \"\"\"\n",
        "        Q-Learning Agent\n",
        "        based on http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html\n",
        "        Instance variables you have access to\n",
        "          - self.epsilon (exploration prob)\n",
        "          - self.alpha (learning rate)\n",
        "          - self.discount (discount rate aka gamma)\n",
        "\n",
        "        Functions you should use\n",
        "          - self.get_legal_actions(state) {state, hashable -> list of actions, each is hashable}\n",
        "            which returns legal actions for a state\n",
        "          - self.get_qvalue(state,action)\n",
        "            which returns Q(state,action)\n",
        "          - self.set_qvalue(state,action,value)\n",
        "            which sets Q(state,action) := value\n",
        "\n",
        "        !!!Important!!!\n",
        "        Note: please avoid using self._qValues directly. \n",
        "            There's a special self.get_qvalue/set_qvalue for that.\n",
        "        \"\"\"\n",
        "\n",
        "        self.get_legal_actions = get_legal_actions\n",
        "        self._qvalues = defaultdict(lambda: defaultdict(lambda: 0))\n",
        "        self.alpha = alpha\n",
        "        self.epsilon = epsilon\n",
        "        self.discount = discount\n",
        "\n",
        "    def get_qvalue(self, state, action):\n",
        "        \"\"\" Returns Q(state,action) \"\"\"\n",
        "        return self._qvalues[state][action]\n",
        "\n",
        "    def set_qvalue(self,state,action,value):\n",
        "        \"\"\" Sets the Qvalue for [state,action] to the given value \"\"\"\n",
        "        self._qvalues[state][action] = value\n",
        "\n",
        "\n",
        "    def get_value(self, state):\n",
        "        \"\"\"\n",
        "        Compute your agent's estimate of V(s) using current q-values\n",
        "        V(s) = max_over_action Q(state,action) over possible actions.\n",
        "        Note: please take into account that q-values can be negative.\n",
        "        \"\"\"\n",
        "        possible_actions = self.get_legal_actions(state)\n",
        "\n",
        "        #If there are no legal actions, return 0.0\n",
        "        if len(possible_actions) == 0:\n",
        "            return 0.0\n",
        "\n",
        "        q = [self.get_qvalue(state,a) for a in possible_actions]\n",
        "        value = max(q)\n",
        "\n",
        "        return value\n",
        "\n",
        "    def update(self, state, action, reward, next_state):\n",
        "        \"\"\"\n",
        "        You should do your Q-Value update here:\n",
        "           Q(s,a) := (1 - alpha) * Q(s,a) + alpha * (r + gamma * V(s'))\n",
        "        \"\"\"\n",
        "\n",
        "        #agent parameters\n",
        "        gamma = self.discount\n",
        "        learning_rate = self.alpha\n",
        "\n",
        "        Q = (1-learning_rate) * self.get_qvalue(state,action) + learning_rate * (reward + gamma * self.get_value(next_state))\n",
        "        \n",
        "        self.set_qvalue(state, action, Q)\n",
        "\n",
        "    \n",
        "    def get_best_action(self, state):\n",
        "        \"\"\"\n",
        "        Compute the best action to take in a state (using current q-values). \n",
        "        \"\"\"\n",
        "        possible_actions = self.get_legal_actions(state)\n",
        "\n",
        "        #If there are no legal actions, return None\n",
        "        if len(possible_actions) == 0:\n",
        "            return None\n",
        "\n",
        "        q = [self.get_qvalue(state,a) for a in possible_actions]\n",
        "        maxQ = max(q)\n",
        "        i = q.index(maxQ)\n",
        "        best_action = possible_actions[i]\n",
        "\n",
        "        return best_action\n",
        "\n",
        "    def get_action(self, state):\n",
        "        \"\"\"\n",
        "        Compute the action to take in the current state, including exploration.  \n",
        "        With probability self.epsilon, we should take a random action.\n",
        "            otherwise - the best policy action (self.getPolicy).\n",
        "        \n",
        "        Note: To pick randomly from a list, use random.choice(list). \n",
        "              To pick True or False with a given probablity, generate uniform number in [0, 1]\n",
        "              and compare it with your probability\n",
        "        \"\"\"\n",
        "\n",
        "        # Pick Action\n",
        "        possible_actions = self.get_legal_actions(state)\n",
        "        action = None\n",
        "\n",
        "        #If there are no legal actions, return None\n",
        "        if len(possible_actions) == 0:\n",
        "            return None\n",
        "\n",
        "        #agent parameters:\n",
        "        epsilon = self.epsilon\n",
        "\n",
        "        if random.random() < epsilon:\n",
        "            action = random.choice(possible_actions)\n",
        "        else:\n",
        "            action = self.get_best_action(state)\n",
        "        chosen_action = action\n",
        "            \n",
        "        return chosen_action"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3gI4HuLllBqU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Demo on taxi\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Here we use the qlearning agent from before on taxi env from openai gym. You will need to insert a few agent functions here."
      ]
    },
    {
      "metadata": {
        "id": "NBIeeSwE5M-D",
        "colab_type": "code",
        "outputId": "c26d28a3-9579-47a8-b435-4a895918f8d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "import gym\n",
        "env = gym.make(\"Taxi-v2\")\n",
        "\n",
        "n_actions = env.action_space.n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
            "  result = entry_point.load(False)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "vIqDKTDJ5X7l",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "agent = QLearningAgent(alpha=0.5, epsilon=0.25, discount=0.99,\n",
        "                       get_legal_actions = lambda s: range(n_actions))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "S92-q9MI5h8B",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def play_and_train(env,agent,t_max=10**4):\n",
        "    \"\"\"\n",
        "    This function should \n",
        "    - run a full game, actions given by agent's e-greedy policy\n",
        "    - train agent using agent.update(...) whenever it is possible\n",
        "    - return total reward\n",
        "    \"\"\"\n",
        "    total_reward = 0.0\n",
        "    s = env.reset()\n",
        "    \n",
        "    for t in range(t_max):\n",
        "        # get agent to pick action given state s.\n",
        "        a = agent.get_action(s)\n",
        "        \n",
        "        next_s, r, done, _ = env.step(a)\n",
        "        \n",
        "        # train (update) agent for state s\n",
        "        agent.update(s,a,r,next_s)\n",
        "        \n",
        "        s = next_s\n",
        "        total_reward +=r\n",
        "        if done: break\n",
        "        \n",
        "    return total_reward\n",
        "    \n",
        "        \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GVJSEPsD5lkg",
        "colab_type": "code",
        "outputId": "e0339e54-2f68-4d1a-f468-f4feeb442d7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "cell_type": "code",
      "source": [
        "from IPython.display import clear_output\n",
        "\n",
        "rewards = []\n",
        "for i in range(1000):\n",
        "    rewards.append(play_and_train(env, agent))\n",
        "    agent.epsilon *= 0.99\n",
        "    \n",
        "    if i %100 ==0:\n",
        "        clear_output(True)\n",
        "        print('eps =', agent.epsilon, 'mean reward =', np.mean(rewards[-10:]))\n",
        "        plt.plot(rewards)\n",
        "        plt.show()\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "eps = 2.9191091959171894e-05 mean reward = 6.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3Ww08ATwHrBQRu4iMwhP025M5bioN5MqQfc2YOff0sXHuRIdulFKpkezJ2LV4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\npZSyOA16pZSyOA16pZSyuP8POEBojd1Rgw0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f08c7ca5a90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Do3tmg3qlPHY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Use agent to train efficiently on CartPole-v0\n",
        "\n",
        "This environment has a continuous number of states, so you will have to group them into bins somehow.\n",
        "\n",
        "The simplest way is to use round(x,n_digits) (or numpy round) to round real number to a given amount of digits.\n",
        "\n",
        "The tricky part is to get the n_digits right for each state to train effectively.\n",
        "\n",
        "Note that you don't need to convert state to integers, but to tuples of any kind of values."
      ]
    },
    {
      "metadata": {
        "id": "3pOY9xFT5ouF",
        "colab_type": "code",
        "outputId": "6a668e27-a6c6-4c73-b3c3-4fec2cc353ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        }
      },
      "cell_type": "code",
      "source": [
        "env = gym.make(\"CartPole-v0\")\n",
        "n_actions = env.action_space.n\n",
        "\n",
        "print(\"first state:%s\" % (env.reset()))\n",
        "plt.imshow(env.render('rgb_array'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
            "  result = entry_point.load(False)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "first state:[-0.01396916 -0.01259991 -0.01809726 -0.02508136]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f08c7defba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4ObAyMw/MQ82SpBn0cp/6PwOXR8Ru4AzgO/Wo/XrgQWAXsMVAl6QTr+uRuiTp5OeKUkkq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\nIIa6JBXEUJekghjqklQQQ12SCvJ/PC+LTPeQxrAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f08ca7b6c18>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "aW5xnHs152Zy",
        "colab_type": "code",
        "outputId": "50190a79-c860-464d-e4f9-ca99461cb686",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1010
        }
      },
      "cell_type": "code",
      "source": [
        "all_states = []\n",
        "for _ in range(1000):\n",
        "    all_states.append(env.reset())\n",
        "    done = False\n",
        "    while not done:\n",
        "        s, r, done, _ = env.step(env.action_space.sample())\n",
        "        all_states.append(s)\n",
        "        if done: break\n",
        "            \n",
        "all_states = np.array(all_states)\n",
        "\n",
        "for obs_i in range(env.observation_space.shape[0]):\n",
        "    plt.hist(all_states[:, obs_i], bins=20)\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2TmrkvgK4IjNfDuyOiOf2OuN4nTJHxGHAu4GTqt/R4yPiFd1+ZhNLfxlwQ3V7A61f4L0i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\nYulLUkEsfUkqiKUvSQX5f36cXW5ZLf+WAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f08c7c08588>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f08c7d6d6d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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y89WIWAfsAMaB9Zk51oG6JUlNanXp5iCwYpKXlk/SdwuwpZX9SJLa571u6ni/Gkkl8hYI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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f08c7d83c88>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klS4XicekTRAVt/3bM/Lblx7RR8r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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f08bd0f47f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Uy61d4ls56kD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from gym.core import ObservationWrapper\n",
        "class Binarizer(ObservationWrapper):\n",
        "    \n",
        "    def _observation(self, state):    \n",
        "        \n",
        "        #state = <round state to some amount digits.>\n",
        "        #hint: you can do that with round(x,n_digits)\n",
        "        #you will need to pick a different n_digits for each dimension\n",
        "        state[0] = np.round(state[0],0)\n",
        "\n",
        "        state[1] = np.round(state[1],1)\n",
        "\n",
        "        state[2] = np.round(state[2],1)\n",
        "\n",
        "        state[3] = np.round(state[3],3)\n",
        "        return tuple(state)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0n4U6nAj5_pn",
        "colab_type": "code",
        "outputId": "724bdfab-9ba5-4b35-eb21-4aa705820180",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "env = Binarizer(gym.make(\"CartPole-v0\"))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gym/envs/registration.py:14: PkgResourcesDeprecationWarning: Parameters to load are deprecated.  Call .resolve and .require separately.\n",
            "  result = entry_point.load(False)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "HLi12JVH6CzY",
        "colab_type": "code",
        "outputId": "5a9af97f-0ce1-46ba-bbd4-3664cc556625",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1028
        }
      },
      "cell_type": "code",
      "source": [
        "all_states = []\n",
        "for _ in range(1000):\n",
        "    all_states.append(env.reset())\n",
        "    done = False\n",
        "    while not done:\n",
        "        s, r, done, _ = env.step(env.action_space.sample())\n",
        "        all_states.append(s)\n",
        "        if done: break\n",
        "            \n",
        "all_states = np.array(all_states)\n",
        "\n",
        "for obs_i in range(env.observation_space.shape[0]):\n",
        "    \n",
        "    plt.hist(all_states[:,obs_i],bins=20)\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[33mWARN: <class '__main__.Binarizer'> doesn't implement 'observation' method. Maybe it implements deprecated '_observation' method.\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f08bd0350f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f08bcc6be10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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dDICdwLIJ918APNylWiSpON0MgC3AGQAR8bPAzszc3cV6JKkoXTsGABARHwJeB+wH3puZ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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f08bcf78e10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f08bcc6bdd8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZTVSecyy6Guo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "agent = QLearningAgent(alpha=0.5, epsilon=0.25, discount=0.99,\n",
        "                       get_legal_actions = lambda s: range(n_actions))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SvjlhlmC6Mft",
        "colab_type": "code",
        "outputId": "0f5e0760-2190-45b4-b62e-dbbd9f517dcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "cell_type": "code",
      "source": [
        "rewards2 = []\n",
        "for i in range(100000):\n",
        "    rewards2.append(play_and_train(env,agent))   \n",
        "    \n",
        "    #OPTIONAL YOUR CODE: adjust epsilon\n",
        "    if i %100 ==0:\n",
        "        clear_output(True)\n",
        "        print('eps =', agent.epsilon, 'mean reward =', np.mean(rewards2[-10:]))\n",
        "        plt.plot(rewards2)\n",
        "        plt.show()\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "eps = 0.25 mean reward = 76.2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Mu+fo16tpMr/nJ8f63e7en5/AzT88nJOnHMKZJ4zgRycf/JwmjujD8RMHNr7v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\nIg6k5C4i4kBK7iIiDvT/AZA6uIaDv9NeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f08b7a7ef98>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "h5MElc516Pvb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}