Mountain_Car_nn_2.ipynb

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        "name": "ipython",
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "Mountain_Car_nn-2.ipynb",
      "provenance": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yxpJsdnlvj2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzbcJg8sltv-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "b31883f0-736c-40d1-c51e-2d5cfa29fda0"
      },
      "source": [
        "import numpy as np\n",
        "import gym\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from IPython import display\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation\n",
        "from keras.optimizers import Adam"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
            "tags": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "nHCYDicyltwo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def build_model(input_size, output_size,learning_rate,final_activation,nodes):\n",
        "    model = Sequential()\n",
        "    model.add(Dense(nodes, input_dim=input_size, activation=\"relu\"))\n",
        "    model.add(Dense(nodes, activation=\"relu\"))\n",
        "    model.add(Dense(nodes, activation=\"relu\"))\n",
        "    model.add(Dense(output_size, activation='linear'))\n",
        "    if final_activation is not None:\n",
        "        model.add(final_activation)\n",
        "    model.compile(loss='mse', optimizer=Adam(lr=learning_rate))\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLVArJYWltw2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_results(x,y,x_label,y_label,title):\n",
        "    \"\"\"\n",
        "    Plot results for each function\n",
        "    :param x: [] list of values to display along X axis\n",
        "    :param y: [] list of values to display  along Y axis\n",
        "    :param x_label: Str name of the x axis\n",
        "    :param y_label: Str name of the y axis\n",
        "    :param title : Str title of the chart\n",
        "    \"\"\"\n",
        "    plt.plot(x,y)\n",
        "    plt.xlabel(x_label)\n",
        "    plt.ylabel(y_label)\n",
        "    plt.title(title)\n",
        "    plt.show()\n",
        "    plt.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "76IsNl-gltxA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def QlearningNN(env, epsilon_decay, discount, epsilon, min_eps, episodes, model, other_stats):\n",
        "    local_stats = {'reached_goal': 0, 'current_moves': 0, 'moves_to_success': [], 'highest_position': -1.2,\n",
        "                   'largest_velocity': -0.07}\n",
        "\n",
        "    # Initialize variables to track rewards\n",
        "    reward_list = []\n",
        "    ave_reward_list = []\n",
        "\n",
        "    # Run Q learning algorithm\n",
        "    for i in range(episodes):\n",
        "        # Initialize parameters\n",
        "        done = False\n",
        "        tot_reward, reward = 0, 0\n",
        "        state = env.reset()\n",
        "\n",
        "        steps = 0\n",
        "        while done != True:\n",
        "            # Render environment for last five episodes\n",
        "            # if i >= (episodes - 20):\n",
        "            #    env.render()\n",
        "            steps += 1\n",
        "            # Decrease epsilon up to epsilon_decay value \n",
        "            epsilon *= epsilon_decay\n",
        "            epsilon = max(min_eps, epsilon)\n",
        "            Q = model.predict(state.reshape(1, len(state)))\n",
        "            # Determine next action based on epsilon \n",
        "            if np.random.rand() <= epsilon:\n",
        "                action = env.action_space.sample()\n",
        "            else:\n",
        "                action = np.argmax(Q)\n",
        "\n",
        "            # Get next state and reward\n",
        "            state2, reward, done, info = env.step(action)\n",
        "\n",
        "            if (state2[0] > local_stats['highest_position']):\n",
        "                local_stats['highest_position'] = state2[0]\n",
        "\n",
        "            if (state2[1] > local_stats['largest_velocity']):\n",
        "                local_stats['largest_velocity'] = state2[1]\n",
        "\n",
        "            # Count only moves\n",
        "            if action != 1:\n",
        "                local_stats['current_moves'] += 1\n",
        "\n",
        "            # Check if we reached the goal \n",
        "            if done and state2[0] >= 0.5:\n",
        "                local_stats['reached_goal'] += 1\n",
        "                local_stats['moves_to_success'].append(local_stats['current_moves'])\n",
        "                local_stats['current_moves'] = 0\n",
        "                target = reward\n",
        "\n",
        "\n",
        "            # Adjust Q value for current state\n",
        "            else:\n",
        "                pred = model.predict(state2.reshape(1, len(state2)))\n",
        "                Q_next = np.max(pred)\n",
        "                target = reward + Q_next * discount\n",
        "\n",
        "            Q[0][action] = target\n",
        "            tot_reward += reward\n",
        "\n",
        "            if steps > 199:\n",
        "                done = True\n",
        "\n",
        "            # Train model with state and action \n",
        "            model.fit(x=state.reshape(1, len(state)), y=Q, epochs=1, verbose=0)\n",
        "\n",
        "        # Track rewards\n",
        "        reward_list.append(tot_reward)\n",
        "\n",
        "        if (i + 1) % 100 == 0:\n",
        "            ave_reward = np.mean(reward_list)\n",
        "            ave_reward_list.append(ave_reward)\n",
        "            reward_list = []\n",
        "            other_stats['reached_goal_episodes'].append(local_stats['reached_goal'])\n",
        "            other_stats['highest_positions'].append(local_stats['highest_position'])\n",
        "            other_stats['largest_velocities'].append(local_stats['largest_velocity'])\n",
        "            if (len(local_stats['moves_to_success']) > 0):\n",
        "                other_stats['ave_moves_to_success'].append(np.mean(local_stats['moves_to_success']))\n",
        "            else:\n",
        "                other_stats['ave_moves_to_success'].append(0)\n",
        "            local_stats = {'reached_goal': 0, 'current_moves': 0, 'moves_to_success': [], 'highest_position': -1.2,\n",
        "                           'largest_velocity': -0.07}\n",
        "\n",
        "        if (i + 1) % 100 == 0:\n",
        "            print('Episode {} Average Reward: {}'.format(i + 1, ave_reward))\n",
        "\n",
        "    env.close()\n",
        "    return ave_reward_list, other_stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TS3WwHTqltxN",
        "colab_type": "text"
      },
      "source": [
        "## Initialize model with initial parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reTsteuqltxS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "369d623e-5e12-4b3b-a506-58d9d1ef03f9"
      },
      "source": [
        "env = gym.make('MountainCar-v0')\n",
        "env.reset()\n",
        "#Initialize dictionary to collect statistics\n",
        "other_stats = {'reached_goal_episodes':[], 'ave_moves_to_success':[],'highest_positions':[], 'largest_velocities':[]}\n",
        "model = build_model(env.observation_space.shape[0],env.action_space.n,0.005,None,20)\n",
        "rewards,other_stats = QlearningNN(env, 0.95, 0.85, 1, 0.01, 2000,model,other_stats)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
            "  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Episode 100 Average Reward: -199.69\n",
            "Episode 200 Average Reward: -199.8\n",
            "Episode 300 Average Reward: -198.98\n",
            "Episode 400 Average Reward: -198.24\n",
            "Episode 500 Average Reward: -199.16\n",
            "Episode 600 Average Reward: -199.38\n",
            "Episode 700 Average Reward: -200.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOUiWwIBltxl",
        "colab_type": "text"
      },
      "source": [
        "### Display main statistics as for DP method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGadhB2Sltxn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5641f8ad-8769-4b8e-a0b3-1ae44a5c23bd"
      },
      "source": [
        "episodes = 100*(np.arange(len(rewards)) + 1)\n",
        "episodes2  = 100*(np.arange(len(other_stats['ave_moves_to_success'])) + 1)\n",
        "plot_results(episodes,rewards, 'Episodes','Average Reward','Average Reward vs Episodes')\n",
        "plot_results(episodes2,other_stats['ave_moves_to_success'], 'Episodes','Number of moves','Average moves to success')\n",
        "plot_results(episodes2,other_stats['largest_velocities'], 'Episodes','Largest_velocity','Largest velocity per batch')\n",
        "plot_results(episodes2,other_stats['highest_positions'], 'Episodes','Highest position','Highest position per batch')\n",
        "plot_results(episodes2,other_stats['reached_goal_episodes'], 'Episodes','Episodes reached goal','Episodes that reached the goal')\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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FexqashJYCPOBb+3nV89PcscVm/jIa3fhbXLS6XMzFdSexkpiKLDILResy/q+z2N4ILrk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\nM7PK/g8fYXqj0edBTQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CJAyy3cl0ew1NHd2D76woijKCUSESBh679El9i5q0FEU5tVEhEgY5bs1aVxRFARUiYZGd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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZzvRGwqpYs1zoXvOD7qRk/YmRKDs2tuG3zeB2u+qvZiy6iEeq210dCvgQqWD4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\nHAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PKuBPXzqJuWXFB+2Tm+Xll5fMZ+poP7/45wds29fCvZ+tjFvmweaelzfy07+/z5HlI/jl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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sFjNuuHw3/umhV3A8FMfuOgS+9KQRmQIFoUfTO2jJovk8EflU7weI6M+MHZag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\nDXwQBEFB+Rdpg8Q1Gckg5gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzLqLW7jltxv",
        "colab_type": "text"
      },
      "source": [
        "### Try different model configurations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zt7VA5jXltxx",
        "colab_type": "text"
      },
      "source": [
        "#### Explore number of nodes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "paLyPp6Ge33X",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwiiGsgpltx0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "outputId": "45989826-f7f0-4ea8-9f37-7702d5f212c0"
      },
      "source": [
        "node_options = [2, 5, 10, 20, 40]\n",
        "reward_per_option = {}\n",
        "for node in node_options:\n",
        "    reward_per_option[node] = []\n",
        "\n",
        "for node in node_options:\n",
        "    model = build_model(env.observation_space.shape[0],env.action_space.n,0.005,None,node)\n",
        "    rewards,other_stats = QlearningNN(env, 0.995, 0.85, 1, 0.01, 1000,model,other_stats)\n",
        "    reward_per_option[node] = rewards\n",
        "\n",
        "df = pd.DataFrame(reward_per_option)\n",
        "ax = plt.gca()\n",
        "for node in node_options:\n",
        "    df.plot(y=node,ax=ax,title=\"Explore  number of nodes per layer\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Episode 10 Average Reward: -200.0\n",
            "Episode 20 Average Reward: -200.0\n",
            "Episode 30 Average Reward: -200.0\n",
            "Episode 40 Average Reward: -200.0\n",
            "Episode 50 Average Reward: -200.0\n",
            "Episode 60 Average Reward: -200.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-b2939531cc4b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode_options\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobservation_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.005\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mother_stats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mQlearningNN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.995\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.95\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mother_stats\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mreward_per_option\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-15-136bbfbcb8ab>\u001b[0m in \u001b[0;36mQlearningNN\u001b[0;34m(env, epsilon_decay, discount, epsilon, min_eps, episodes, model, other_stats)\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0mepsilon\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mepsilon_decay\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m             \u001b[0mepsilon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_eps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m             \u001b[0mQ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m             \u001b[0;31m# Determine next action based on epsilon\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1399\u001b[0m                                             \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1400\u001b[0m                                             \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1401\u001b[0;31m                                             callbacks=callbacks)\n\u001b[0m\u001b[1;32m   1402\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1403\u001b[0m     def train_on_batch(self, x, y,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mpredict_loop\u001b[0;34m(model, f, ins, batch_size, verbose, steps, callbacks)\u001b[0m\n\u001b[1;32m    330\u001b[0m             \u001b[0mbatch_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'batch'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'size'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'predict'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'begin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2977\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2979\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2980\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2981\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2935\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2936\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2937\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2938\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1470\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m   1471\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1472\u001b[0;31m                                                run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1473\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyfmFdetltx8",
        "colab_type": "text"
      },
      "source": [
        "#### Explore learning rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJlygZ40ltx9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lr_options = [0.0001,0.0005,0.001,0.1,0.15]\n",
        "reward_per_option = {}\n",
        "for lr in lr_options:\n",
        "    reward_per_option[lr] = []\n",
        "\n",
        "for lr in lr_options:\n",
        "    model = build_model(env.observation_space.shape[0],env.action_space.n,lr,None,20)\n",
        "    rewards,other_stats = QlearningNN(env, 0.995, 0.85, 1, 0.01, 1000,model,other_stats)\n",
        "    reward_per_option[lr] = rewards\n",
        "\n",
        "df = pd.DataFrame(reward_per_option)\n",
        "ax = plt.gca()\n",
        "for lr in lr_options:\n",
        "    df.plot(y=lr,ax=ax,title=\"Explore learning rate values\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZxOHWIiltyC",
        "colab_type": "text"
      },
      "source": [
        "#### Explore final activations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "safjXltvltyE",
        "colab_type": "text"
      },
      "source": [
        "#####  Check for advanced activation functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tyZpmo4ltyH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
        "activation_options = [LeakyReLU(alpha=.001), PReLU()]\n",
        "activation_option_names = ['leaky_relu', 'prelu']\n",
        "\n",
        "for activation in activation_option_names:\n",
        "    reward_per_option[activation] = []\n",
        "\n",
        "for i in range(0,2):\n",
        "    activation = activation_options[i]\n",
        "    activation_name = activation_option_names[i]\n",
        "    model = build_model(env.observation_space.shape[0],env.action_space.n,0.005,activation,20)\n",
        "    rewards,other_stats = QlearningNN(env, 0.995, 0.85, 1, 0.01, 1000,model,other_stats)\n",
        "    reward_per_option[activation_name] = rewards\n",
        "\n",
        "df = pd.DataFrame(reward_per_option)\n",
        "ax = plt.gca()\n",
        "for activation_name in activation_option_names:\n",
        "    df.p"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}