comparison_environment.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "fMoYCBo--BD3"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sj6ZtOFP9_z7"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import json\n",
        "\n",
        "plt.style.use('default')\n",
        "from matplotlib import font_manager\n",
        "font_prop = font_manager.FontProperties(family='fantasy')\n",
        "legend_font_prop = font_manager.FontProperties(family='fantasy', size=14)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HBPrHnf-2wVX",
        "outputId": "3d78a14e-1f7d-4dd9-9848-e9523dc12ffe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# No need to run this cell if you are not using Google Drive and you have all the files in the same folder as this notebook\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "iFxEWkbl62i3"
      },
      "source": [
        "## Comparison environment"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "w0sdMk9p_nxr"
      },
      "source": [
        "### Generate random results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-3RGBldmwZMt"
      },
      "outputs": [],
      "source": [
        "def load_json(file_name):\n",
        "  with open(file_name, 'r') as f:\n",
        "    data = json.load(f)\n",
        "    data['episode'] = len(data['runtime'])\n",
        "    # change to minutes\n",
        "    data['runtime'] = [round(sec/60, 2) for sec in data['runtime']]\n",
        "    return data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VAJelzSy2wy6"
      },
      "outputs": [],
      "source": [
        "random_agent = load_json('/content/drive/MyDrive/minecraft proj/For comparison/stats_random.json') #change all these paths to your own\n",
        "bc_agent = load_json('/content/drive/MyDrive/minecraft proj/For comparison/stats_BC.json')\n",
        "imi_agent = load_json('/content/drive/MyDrive/minecraft proj/For comparison/stats_imi_compare.json')\n",
        "scripted_agent = load_json('/content/drive/MyDrive/minecraft proj/For comparison/stats_full_scripted.json')\n",
        "learned_agent = load_json('/content/drive/MyDrive/minecraft proj/For comparison/stats_learned_actions.json')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "CmOd_hM2_v2v"
      },
      "source": [
        "### Compare"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "npTQoX4MYhX4"
      },
      "outputs": [],
      "source": [
        "colors = ['#b02e26', '#169c9d', '#825432', '#80c71f']\n",
        "runtime = {}\n",
        "reward = {}\n",
        "for (c, agent, name) in [(colors[0], random_agent, 'random'), (colors[1], bc_agent, 'BC'), (colors[2], imi_agent, 'imitation'), (colors[3], scripted_agent, 'scripted'), ('', learned_agent, 'learned')]:\n",
        "  runtime[name] = {i + 1: agent['runtime'][i] for i in range(len(agent['runtime']))}\n",
        "  reward[name] = {i + 1: agent['reward'][i] for i in range(len(agent['reward']))}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "OgH75IsWdcs0",
        "outputId": "99aab15f-9224-46ac-c57f-003e23b31f53"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# set width of bar\n",
        "barWidth = 0.20\n",
        "fig, ax = plt.subplots(figsize =(12, 6))\n",
        "\n",
        "# set height of bar\n",
        "random_stats = [np.mean(random_agent[i]) for i in ['episode', 'runtime','reward']]\n",
        "bc_stats = [np.mean(bc_agent[i]) for i in ['episode', 'runtime','reward']]\n",
        "imi_stats = [np.mean(imi_agent[i]) for i in ['episode', 'runtime','reward']]\n",
        "scripted_stats = [np.mean(scripted_agent[i]) for i in ['episode', 'runtime','reward']]\n",
        "\n",
        "# Set position of bar on X axis\n",
        "br1 = np.arange(len(random_stats))\n",
        "br2 = [x + barWidth for x in br1]\n",
        "br3 = [x + barWidth for x in br2]\n",
        "br4 = [x + barWidth for x in br3]\n",
        "\n",
        "# Make the plot\n",
        "plt.bar(br1, random_stats, color=colors[0], width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='Random')\n",
        "plt.bar(br2, bc_stats, color=colors[1], width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='Behavioral Cloning')\n",
        "plt.bar(br3, imi_stats, color =colors[2], width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='Imitation Learning')\n",
        "plt.bar(br4, scripted_stats, color =colors[3], width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='Scripted-Actions')\n",
        "\n",
        "# Adding Xticks\n",
        "plt.title('General Measurements Per Agent Type', fontproperties=font_prop, size=15)\n",
        "\n",
        "plt.xlabel('Measurement Type', fontproperties=font_prop, size=12)\n",
        "plt.xticks([r + barWidth for r in range(len(random_stats))],\n",
        "\t\t['total episode', 'mean survival time', 'mean reward'], fontproperties=font_prop, size=10)\n",
        "\n",
        "plt.legend(loc='upper left', prop=legend_font_prop)\n",
        "\n",
        "ax.set_facecolor('#EAEAF2')\n",
        "ax.grid(True)\n",
        "ax.set_axisbelow(True)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "id": "TQbrAvCBwXDj",
        "outputId": "dddf3bde-f1ec-4de5-9831-3ecc27891703"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from collections import Counter\n",
        "\n",
        "barWidth = 0.33\n",
        "x = np.array([0.0, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0, 256.0])\n",
        "fig, ax = plt.subplots(figsize=(20,10))\n",
        "for i, agent in enumerate([bc_agent, imi_agent, scripted_agent]):\n",
        "    agent_reward_step = agent['reward_at']\n",
        "    c=Counter()\n",
        "    # Iterate over each sublist in the given data\n",
        "    for sublist in agent_reward_step:\n",
        "        if sublist:\n",
        "            c[sublist[-1][1]]+=1\n",
        "        \n",
        "    y=[c[i] for i in x if i != 0.0]\n",
        "    y.insert(0, len(agent_reward_step) - sum(y))\n",
        "    \n",
        "\n",
        "# Plot the bar chart with colors based on the normalized reward values\n",
        "    x_offset = i * barWidth\n",
        "    plt.bar(np.arange(len(x)) + x_offset, y, color=colors[i + 1], width=barWidth, label='Behavioral Cloning' if i == 0 else 'Imitation Learning' if i == 1 else 'Scripted')\n",
        "    \n",
        "plt.xlabel('Maximal Stage Reached', size=20, fontproperties=font_prop)\n",
        "plt.ylabel('Amount of episodes', size=20, fontproperties=font_prop)\n",
        "plt.suptitle('Amount of episodes reached each task stage', fontproperties=font_prop, size=25)\n",
        "plt.xticks(np.arange(len(x)) + 2*barWidth, ['no reward', 'log', 'planks', 'sticks', 'wooden pickaxe', 'cobblestone', 'stone pickaxe', 'iron ore', 'iron ingot', 'iron pickaxe'], fontproperties=font_prop, size=12)\n",
        "plt.yticks(np.arange(11.0), fontproperties=font_prop, size=12)\n",
        "plt.title('each stage contains all the previous stages', fontproperties=font_prop, size=20)\n",
        "plt.legend(prop=legend_font_prop)\n",
        "\n",
        "ax.set_facecolor('#EAEAF2')\n",
        "ax.grid(True)\n",
        "ax.set_axisbelow(True)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kOjwZWWy05ok"
      },
      "outputs": [],
      "source": [
        "random_agents_df = pd.DataFrame.from_dict(random_agent)\n",
        "bc_df = pd.DataFrame.from_dict(bc_agent)\n",
        "imi_df = pd.DataFrame.from_dict(imi_agent)\n",
        "scripted_df = pd.DataFrame.from_dict(scripted_agent)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "EiH12u2W8-m5",
        "outputId": "b444413a-254d-410c-9e5e-6722f07ee7b9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-27432618-7e8b-4e95-84bd-c7b3ffc11510\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>runtime</th>\n",
              "      <th>reward</th>\n",
              "      <th>episode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.747000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.046537</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.120000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.325000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10.285000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.572500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.270000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27432618-7e8b-4e95-84bd-c7b3ffc11510')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         runtime  reward  episode\n",
              "count  10.000000    10.0     10.0\n",
              "mean    9.747000     0.0     10.0\n",
              "std     5.046537     0.0      0.0\n",
              "min     3.120000     0.0     10.0\n",
              "25%     5.325000     0.0     10.0\n",
              "50%    10.285000     0.0     10.0\n",
              "75%    13.572500     0.0     10.0\n",
              "max    17.270000     0.0     10.0"
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          },
          "execution_count": 9,
          "metadata": {},
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              "         runtime     reward  episode\n",
              "count  10.000000  10.000000     10.0\n",
              "mean    8.787000   2.100000     10.0\n",
              "std     5.829584   3.381321      0.0\n",
              "min     1.580000   0.000000     10.0\n",
              "25%     4.915000   0.000000     10.0\n",
              "50%     6.510000   0.000000     10.0\n",
              "75%    13.832500   5.250000     10.0\n",
              "max    17.820000   7.000000     10.0"
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              "      <td>10.000000</td>\n",
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              "      <td>18.761000</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         runtime      reward  episode\n",
              "count  10.000000   10.000000     10.0\n",
              "mean   18.761000   29.000000     10.0\n",
              "std     5.635326   38.099285      0.0\n",
              "min    10.690000    3.000000     10.0\n",
              "25%    14.827500    7.000000     10.0\n",
              "50%    17.930000   15.000000     10.0\n",
              "75%    23.135000   35.000000     10.0\n",
              "max    26.950000  131.000000     10.0"
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          "metadata": {},
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              "      <th>runtime</th>\n",
              "      <th>reward</th>\n",
              "      <th>episode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.967000</td>\n",
              "      <td>26.300000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.691522</td>\n",
              "      <td>57.207323</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.090000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.332500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.985000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.380000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.070000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9ef0a13-6388-46e0-86c7-b60e53a5ef33')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a9ef0a13-6388-46e0-86c7-b60e53a5ef33');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         runtime      reward  episode\n",
              "count  10.000000   10.000000     10.0\n",
              "mean    2.967000   26.300000     10.0\n",
              "std     1.691522   57.207323      0.0\n",
              "min     1.090000    0.000000     10.0\n",
              "25%     1.332500    0.000000     10.0\n",
              "50%     2.985000    0.000000     10.0\n",
              "75%     4.380000    0.750000     10.0\n",
              "max     5.070000  163.000000     10.0"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scripted_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "oRGEhFmRxj1a",
        "outputId": "19eff21d-12f3-4e1c-d819-a150aa9e0f3f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b00b7c2a-95c9-49a4-9ba4-3a8e3f3d3b84\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Agent</th>\n",
              "      <th>Survival time</th>\n",
              "      <th>Total Reward</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Behavioral Cloning</td>\n",
              "      <td>8.79 ± 5.83</td>\n",
              "      <td>2.10 ± 3.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Imitation Learning</td>\n",
              "      <td>18.76 ± 5.64</td>\n",
              "      <td>29.00 ± 38.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Learned-Actions</td>\n",
              "      <td>86.31 ± 62.28</td>\n",
              "      <td>0.00 ± 0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random</td>\n",
              "      <td>9.75 ± 5.05</td>\n",
              "      <td>0.00 ± 0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Scripted-Actions</td>\n",
              "      <td>2.97 ± 1.69</td>\n",
              "      <td>26.30 ± 57.21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b00b7c2a-95c9-49a4-9ba4-3a8e3f3d3b84')\"\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b00b7c2a-95c9-49a4-9ba4-3a8e3f3d3b84');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                Agent  Survival time   Total Reward\n",
              "0  Behavioral Cloning    8.79 ± 5.83    2.10 ± 3.38\n",
              "1  Imitation Learning   18.76 ± 5.64  29.00 ± 38.10\n",
              "2     Learned-Actions  86.31 ± 62.28    0.00 ± 0.00\n",
              "3              Random    9.75 ± 5.05    0.00 ± 0.00\n",
              "4    Scripted-Actions    2.97 ± 1.69  26.30 ± 57.21"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Define the data for each agent\n",
        "agents_data = [\n",
        "    {\n",
        "        'name': 'Random',\n",
        "        'episode_to_runtime': runtime['random'],  # Dictionary mapping episode to runtime for Agent 1\n",
        "        'episode_to_total_reward': reward['random']  # Dictionary mapping episode to total reward for Agent 1\n",
        "    },\n",
        "    {\n",
        "        'name': 'Behavioral Cloning',\n",
        "        'episode_to_runtime': runtime['BC'],  # Dictionary mapping episode to runtime for Agent 2\n",
        "        'episode_to_total_reward': reward['BC']  # Dictionary mapping episode to total reward for Agent 2\n",
        "    },\n",
        "    {\n",
        "        'name': 'Imitation Learning',\n",
        "        'episode_to_runtime': runtime['imitation'],  # Dictionary mapping episode to runtime for Agent 1\n",
        "        'episode_to_total_reward': reward['imitation']  # Dictionary mapping episode to total reward for Agent 1\n",
        "    },\n",
        "    {\n",
        "        'name': 'Scripted-Actions',\n",
        "        'episode_to_runtime': runtime['scripted'],  # Dictionary mapping episode to runtime for Agent 2\n",
        "        'episode_to_total_reward': reward['scripted']  # Dictionary mapping episode to total reward for Agent 2\n",
        "    }\n",
        "]\n",
        "\n",
        "# Create a DataFrame for each agent\n",
        "agent_dfs = []\n",
        "for agent_data in agents_data:\n",
        "    agent_name = agent_data['name']\n",
        "    episode_to_runtime = agent_data['episode_to_runtime']\n",
        "    episode_to_total_reward = agent_data['episode_to_total_reward']\n",
        "    agent_df = pd.DataFrame({\n",
        "        'Episode': list(episode_to_runtime.keys()),\n",
        "        'Survival time': list(episode_to_runtime.values()),\n",
        "        'Total Reward': list(episode_to_total_reward.values())\n",
        "    })\n",
        "    agent_df['Agent'] = agent_name\n",
        "    agent_dfs.append(agent_df)\n",
        "\n",
        "# Concatenate all agent DataFrames into a single DataFrame\n",
        "combined_df = pd.concat(agent_dfs)\n",
        "\n",
        "# Calculate the mean and standard deviation for each agent and metric\n",
        "summary_df = combined_df.groupby('Agent').agg({\n",
        "    'Survival time': lambda x: f\"{x.mean():.2f} ± {x.std():.2f}\",\n",
        "    'Total Reward': lambda x: f\"{x.mean():.2f} ± {x.std():.2f}\"\n",
        "}).reset_index()\n",
        "\n",
        "# Print the summary table\n",
        "summary_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M62wtywCnZnF",
        "outputId": "d9013930-107e-44e1-dbca-39609b5fcc14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/MyDrive/minecraft proj/For comparison/minerl imgs.zip\n",
            "  inflating: minerl imgs/8287159cc4065333242e159bcf24c205.png  \n",
            " extracting: minerl imgs/Crafting_Table_JE4_BE3.webp  \n",
            " extracting: minerl imgs/Furnace_29_JE4.webp  \n",
            " extracting: minerl imgs/Iron_Ore_JE6_BE4.webp  \n",
            " extracting: minerl imgs/Oak_Log_29_JE5_BE3.webp  \n",
            "  inflating: minerl imgs/Oak_Planks_JE5.webp  \n",
            "  inflating: minerl imgs/Stick_JE1_BE1.webp  \n",
            " extracting: minerl imgs/Stone_JE5_move.webp  \n",
            " extracting: minerl imgs/Stone_Pickaxe_JE2_BE2.webp  \n"
          ]
        }
      ],
      "source": [
        "!unzip '/content/drive/MyDrive/minecraft proj/For comparison/minerl imgs.zip' #change to your path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "43qHoDl0lCw2"
      },
      "outputs": [],
      "source": [
        "import matplotlib.image as image \n",
        "log = image.imread('/content/minerl imgs/Oak_Log_29_JE5_BE3.webp')\n",
        "plank = image.imread('/content/minerl imgs/Oak_Planks_JE5.webp')\n",
        "stick = image.imread('/content/minerl imgs/Stick_JE1_BE1.webp')\n",
        "crafting = image.imread('/content/minerl imgs/Crafting_Table_JE4_BE3.webp')\n",
        "wooden = image.imread('/content/minerl imgs/8287159cc4065333242e159bcf24c205.png')\n",
        "cobble = image.imread('/content/minerl imgs/Stone_JE5_move.webp')\n",
        "stone = image.imread('/content/minerl imgs/Stone_Pickaxe_JE2_BE2.webp')\n",
        "furnace = image.imread('/content/minerl imgs/Furnace_29_JE4.webp')\n",
        "iron = image.imread('/content/minerl imgs/Iron_Ore_JE6_BE4.webp')\n",
        "id2pic = {1.0: log, 2.0: plank, 4.0: crafting, 5.0: stick, 8.0: wooden, 16.0: cobble, 32.0: stone, 33.0: furnace, 64.0: iron}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778
        },
        "id": "sucwimHgGhId",
        "outputId": "e0357583-bc71-49ba-b625-ad13692a6c4c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
        "import matplotlib.colors as mcolors\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "\n",
        "# Data\n",
        "for i, agent in enumerate([bc_agent, imi_agent, scripted_agent]):\n",
        "    rewards = {}\n",
        "    x = []\n",
        "    y = []\n",
        "    agent_reward_step = agent['reward_at']\n",
        "    for sublist in agent_reward_step:\n",
        "        if sublist:\n",
        "            prev_reward = 0\n",
        "            for item in sublist:\n",
        "                curr_reward = item[1]\n",
        "                if prev_reward == item[1]:\n",
        "                    curr_reward += 1\n",
        "                if curr_reward in rewards:\n",
        "                    rewards[curr_reward] = (rewards[curr_reward][0] + item[0], rewards[curr_reward][1] + 1) \n",
        "                else:\n",
        "                    rewards[curr_reward] = (item[0], 1)\n",
        "                prev_reward = curr_reward\n",
        "\n",
        "    y_min, y_max = [], []\n",
        "\n",
        "    for r, (sum_steps, num_r) in rewards.items():\n",
        "        y.append(r)\n",
        "        x.append(sum_steps / num_r)\n",
        "        y_min.append(r-num_r/2)\n",
        "        y_max.append(r+num_r/2)\n",
        "\n",
        "    plt.plot(x, y, color=colors[i + 1], label='Behavioral Cloning' if i == 0 else 'Imitation Learning' if i == 1 else 'Scripted', lw = 2)\n",
        "    color_rgb = mcolors.hex2color(colors[i + 1])  # Convert hexadecimal color to RGB tuple\n",
        "\n",
        "    # Adjust brightness by multiplying each RGB component\n",
        "    lighter_color_rgb = tuple(min(c * 1.2, 1.0) for c in color_rgb)\n",
        "\n",
        "    # Adjust opacity by setting a lower alpha value\n",
        "    lighter_color_rgba = lighter_color_rgb + (0.5,)  # Add an alpha value (e.g., 0.5 for semi-transparency)\n",
        "\n",
        "    # Convert the adjusted colors back to hexadecimal notation if needed\n",
        "    lighter_color_hex = mcolors.rgb2hex(lighter_color_rgb)\n",
        "    lighter_color_hex_with_alpha = mcolors.rgb2hex(lighter_color_rgba)\n",
        "    # Plot the shaded region between the min and max borders\n",
        "    plt.fill_between(x, y_min, y_max, color=lighter_color_hex_with_alpha, alpha=0.5, label='Episodes ' + ('Behavioral Cloning' if i == 0 else 'Imitation Learning' if i == 1 else 'Scripted'))\n",
        "\n",
        "    # Customize the plot\n",
        "    plt.xlabel('Steps', fontproperties=font_prop, size=15)\n",
        "    plt.ylabel('Reward', fontproperties=font_prop, size=15)\n",
        "    \n",
        "    plt.suptitle('Item obtaining rate throughout an episode', fontproperties=font_prop, size=20)\n",
        "    plt.title('Average number of steps for an agent to get reward that represents obtaining an item', fontproperties=font_prop, size=13)\n",
        "    plt.legend()\n",
        "    if x:\n",
        "        for idx in range(len(x)):\n",
        "            image_box = OffsetImage(id2pic[y[idx]], zoom=0.1 if y[idx] != 32.0 else 0.2)\n",
        "            ab = AnnotationBbox(image_box, (x[idx], y[idx] if y[idx] not in [5.0, 33.0] else y[idx] + 5), frameon=False)\n",
        "            ax.add_artist(ab)\n",
        "\n",
        "ax.set_facecolor('#EAEAF2')\n",
        "ax.grid(True)\n",
        "ax.set_axisbelow(True)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7fH1PKorpmkb"
      },
      "outputs": [],
      "source": [
        "def return_index_plot(agent):\n",
        "    # create data points for plot per agent\n",
        "    rewards = {}\n",
        "    x = []\n",
        "    y = []\n",
        "    agent_reward_step = agent['reward_at']\n",
        "    for sublist in agent_reward_step:\n",
        "        if sublist:\n",
        "            prev_reward = 0\n",
        "            for item in sublist:\n",
        "                curr_reward = item[1]\n",
        "                if prev_reward == item[1]:\n",
        "                    curr_reward += 1\n",
        "                if curr_reward in rewards:\n",
        "                    rewards[curr_reward] = (rewards[curr_reward][0] + item[0], rewards[curr_reward][1] + 1) \n",
        "                else:\n",
        "                    rewards[curr_reward] = (item[0], 1)\n",
        "                prev_reward = curr_reward\n",
        "\n",
        "    y_min, y_max = [], []\n",
        "\n",
        "    for r, (sum_steps, num_r) in rewards.items():\n",
        "        y.append(r)\n",
        "        x.append(sum_steps / num_r)\n",
        "        y_min.append(r-num_r/2)\n",
        "        y_max.append(r+num_r/2)\n",
        "    return((x,y),(y_min,y_max))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q19me28KpvCN"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
        "import matplotlib.colors as mcolors\n",
        "\n",
        "def create_frame(t,x,y,y_min,y_max,id,i_agent,t_x,t_y):\n",
        "    i=i_agent\n",
        "    fig, ax = plt.subplots(figsize=(12,8))\n",
        "    # Data\n",
        "   \n",
        "    plt.plot(x[:(t+1)], y[:(t+1)], color=colors[i + 1], label='Behavioral Cloning' if i == 0 else 'Imitation Learning' if i == 1 else 'Scripted')\n",
        "    color_rgb = mcolors.hex2color(colors[i + 1])  # Convert hexadecimal color to RGB tuple\n",
        "\n",
        "    # Adjust brightness by multiplying each RGB component\n",
        "    lighter_color_rgb = tuple(min(c * 1.2, 1.0) for c in color_rgb)\n",
        "\n",
        "    # Adjust opacity by setting a lower alpha value\n",
        "    lighter_color_rgba = lighter_color_rgb + (0.5,)  # Add an alpha value (e.g., 0.5 for semi-transparency)\n",
        "\n",
        "    # Convert the adjusted colors back to hexadecimal notation if needed\n",
        "    lighter_color_hex = mcolors.rgb2hex(lighter_color_rgb)\n",
        "    lighter_color_hex_with_alpha = mcolors.rgb2hex(lighter_color_rgba)\n",
        "    # Plot the shaded region between the min and max borders\n",
        "    plt.fill_between(x[:t+1], y_min[:t+1], y_max[:t+1], color=lighter_color_hex_with_alpha, alpha=0.5, label='Episodes ' + ('Behavioral Cloning' if i == 0 else 'Imitation Learning' if i == 1 else 'Scripted'))\n",
        "\n",
        "    # Customize the plot\n",
        "    plt.xlabel('Steps', fontproperties=font_prop, size=15)\n",
        "    plt.ylabel('Reward', fontproperties=font_prop, size=15)\n",
        "\n",
        "    plt.xticks(t_x)\n",
        "    plt.yticks(t_y)\n",
        "\n",
        "\n",
        "    \n",
        "    plt.title('Average steps between stages', fontproperties=font_prop, size=15)\n",
        "    plt.legend()\n",
        "    if x:\n",
        "        for idx in range(len(x[:t+1])):\n",
        "            image_box = OffsetImage(id2pic[y[idx]], zoom=0.1 if y[idx] != 32.0 else 0.2)\n",
        "            ab = AnnotationBbox(image_box, (x[idx], y[idx] if y[idx] not in [5.0, 33.0] else y[idx] + 5), frameon=False)\n",
        "            ax.add_artist(ab)\n",
        "    ax.set_facecolor('#EAEAF2')\n",
        "    ax.grid(True)\n",
        "    ax.set_axisbelow(True)\n",
        "    plt.savefig(f\"./img/img_{id}.png\",transparent=False,facecolor=\"white\")\n",
        "\n",
        "    plt.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LoKIJgHep0Pt"
      },
      "outputs": [],
      "source": [
        "all_x=[]\n",
        "all_y=[]\n",
        "\n",
        "for i, agent in enumerate([bc_agent, imi_agent, scripted_agent]):\n",
        "    (x,y),_=return_index_plot(agent)\n",
        "    all_x.extend(x)\n",
        "    all_y.extend(y)\n",
        "    \n",
        "all_x=np.unique(all_x)\n",
        "all_y=np.unique(all_y)\n",
        "all_x.sort()\n",
        "all_y.sort()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "itH7ri6Pp3tn"
      },
      "outputs": [],
      "source": [
        "# create frames for gif\n",
        "!rm -rf img\n",
        "!mkdir img\n",
        "\n",
        "idx=0\n",
        "ids=[]\n",
        "for i, agent in enumerate([bc_agent, imi_agent, scripted_agent]):\n",
        "    (x,y),(y_min,y_max)=return_index_plot(agent)\n",
        "    times=list(range(len(x)))\n",
        "    for t in times:\n",
        "        ids.append(idx)\n",
        "        create_frame(t,x,y,y_min,y_max,idx,i,np.linspace(0,14000,8),np.linspace(0,80,9))\n",
        "        idx+=1\n",
        "\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
        "import matplotlib.colors as mcolors\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "# Data\n",
        "for i, agent in enumerate([bc_agent, imi_agent, scripted_agent]):\n",
        "    rewards = {}\n",
        "    x = []\n",
        "    y = []\n",
        "    agent_reward_step = agent['reward_at']\n",
        "    for sublist in agent_reward_step:\n",
        "        if sublist:\n",
        "            prev_reward = 0\n",
        "            for item in sublist:\n",
        "                curr_reward = item[1]\n",
        "                if prev_reward == item[1]:\n",
        "                    curr_reward += 1\n",
        "                if curr_reward in rewards:\n",
        "                    rewards[curr_reward] = (rewards[curr_reward][0] + item[0], rewards[curr_reward][1] + 1) \n",
        "                else:\n",
        "                    rewards[curr_reward] = (item[0], 1)\n",
        "                prev_reward = curr_reward\n",
        "\n",
        "    y_min, y_max = [], []\n",
        "\n",
        "    for r, (sum_steps, num_r) in rewards.items():\n",
        "        y.append(r)\n",
        "        x.append(sum_steps / num_r)\n",
        "        y_min.append(r-num_r/2)\n",
        "        y_max.append(r+num_r/2)\n",
        "\n",
        "    plt.plot(x, y, color=colors[i + 1], label='Behavioral Cloning' if i == 0 else 'Imitation Learning' if i == 1 else 'Scripted')\n",
        "    color_rgb = mcolors.hex2color(colors[i + 1])  # Convert hexadecimal color to RGB tuple\n",
        "\n",
        "    # Adjust brightness by multiplying each RGB component\n",
        "    lighter_color_rgb = tuple(min(c * 1.2, 1.0) for c in color_rgb)\n",
        "\n",
        "    # Adjust opacity by setting a lower alpha value\n",
        "    lighter_color_rgba = lighter_color_rgb + (0.5,)  # Add an alpha value (e.g., 0.5 for semi-transparency)\n",
        "\n",
        "    # Convert the adjusted colors back to hexadecimal notation if needed\n",
        "    lighter_color_hex = mcolors.rgb2hex(lighter_color_rgb)\n",
        "    lighter_color_hex_with_alpha = mcolors.rgb2hex(lighter_color_rgba)\n",
        "    # Plot the shaded region between the min and max borders\n",
        "    plt.fill_between(x, y_min, y_max, color=lighter_color_hex_with_alpha, alpha=0.5, label='Episodes ' + ('Behavioral Cloning' if i == 0 else 'Imitation Learning' if i == 1 else 'Scripted'))\n",
        "\n",
        "    # Customize the plot\n",
        "    plt.xlabel('Steps', fontproperties=font_prop, size=15)\n",
        "    plt.ylabel('Reward', fontproperties=font_prop, size=15)\n",
        "\n",
        "    plt.xticks(np.linspace(0,14000,8))\n",
        "    plt.yticks(np.linspace(0,80,9))\n",
        "    \n",
        "    plt.suptitle('Average steps between stages', fontproperties=font_prop, size=20)\n",
        "    plt.title('Item obtaining rate throughout the episode', fontproperties=font_prop, size=15)\n",
        "    plt.legend()\n",
        "    if x:\n",
        "        for idx2 in range(len(x)):\n",
        "            image_box = OffsetImage(id2pic[y[idx2]], zoom=0.1 if y[idx2] != 32.0 else 0.2)\n",
        "            ab = AnnotationBbox(image_box, (x[idx2], y[idx2] if y[idx2] not in [5.0, 33.0] else y[idx2] + 5), frameon=False)\n",
        "            ax.add_artist(ab)\n",
        "ax.set_facecolor('#EAEAF2')\n",
        "ax.grid(True)\n",
        "ax.set_axisbelow(True)\n",
        "for i in range(21):\n",
        "    plt.savefig(f\"./img/img_{idx}.png\",transparent=False,facecolor=\"white\")\n",
        "    ids.append(idx)\n",
        "    idx+=1\n",
        "plt.close()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TV0WLAbop63D"
      },
      "outputs": [],
      "source": [
        "# create gif\n",
        "import imageio\n",
        "frames=[]\n",
        "for i in ids:\n",
        "    image=imageio.v2.imread(f\"./img/img_{i}.png\")\n",
        "    frames.append(image)\n",
        "imageio.mimsave(\"./avg.gif\",frames,fps=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "id": "4l8IHxuzgzau",
        "outputId": "be8fbe52-eaae-480a-8b01-628cea1e3289"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "imitation_agent_10 = load_json('/content/drive/MyDrive/minecraft proj/For comparison/stats_imi_10.json')\n",
        "imitation_agent_15 = load_json('/content/drive/MyDrive/minecraft proj/For comparison/stats_imi_15.json') # change these paths to your paths\n",
        "\n",
        "# set width of bar\n",
        "barWidth = 0.25\n",
        "fig, ax = plt.subplots(figsize =(15, 8))\n",
        "\n",
        "# set height of bar\n",
        "imi_10_stats = [np.mean(imitation_agent_10[i]) for i in ['episode', 'runtime','reward']]\n",
        "imi_15_stats = [np.mean(imitation_agent_15[i]) for i in ['episode', 'runtime','reward']]\n",
        "imi_stats = [np.mean(imi_agent[i]) for i in ['episode', 'runtime','reward']]\n",
        "\n",
        "# Set position of bar on X axis\n",
        "br1 = np.arange(len(random_stats))\n",
        "br2 = [x + barWidth for x in br1]\n",
        "br3 = [x + barWidth for x in br2]\n",
        "\n",
        "# Make the plot\n",
        "plt.bar(br1, imi_10_stats, color=colors[0], width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='Imitation 10 epochs')\n",
        "plt.bar(br2, imi_15_stats, color=colors[1], width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='Imitation 15 epochs')\n",
        "plt.bar(br3, imi_stats, color =colors[2], width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='Imitation 20 epochs')\n",
        "\n",
        "# Adding Xticks\n",
        "plt.title('General Measurements For Imitation Learning throughout the learning process', fontproperties=font_prop, size=15)\n",
        "\n",
        "plt.xlabel('Measurement Type', fontproperties=font_prop, size=12)\n",
        "plt.xticks([r + barWidth for r in range(len(random_stats))],\n",
        "\t\t['total episode', 'mean survival time', 'mean reward'], fontproperties=font_prop, size=10)\n",
        "\n",
        "plt.legend(prop=legend_font_prop)\n",
        "\n",
        "ax.set_facecolor('#EAEAF2')\n",
        "ax.grid(True)\n",
        "ax.set_axisbelow(True)\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "id": "3TufQsBdiu0d",
        "outputId": "5d37933c-bdf4-49ed-d076-12e7578d0342"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "barWidth = 0.33\n",
        "x = np.array([0.0, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0, 256.0])\n",
        "fig, ax = plt.subplots(figsize=(20,10))\n",
        "for i, agent in enumerate([imitation_agent_10, imitation_agent_15, imi_agent]):\n",
        "    agent_reward_step = agent['reward_at']\n",
        "    c=Counter()\n",
        "    # Iterate over each sublist in the given data\n",
        "    for sublist in agent_reward_step:\n",
        "        if sublist:\n",
        "            c[sublist[-1][1]]+=1\n",
        "        \n",
        "    y=[c[i] for i in x if i != 0.0]\n",
        "    y.insert(0, len(agent_reward_step) - sum(y))\n",
        "    \n",
        "\n",
        "# Plot the bar chart with colors based on the normalized reward values\n",
        "    x_offset = i * barWidth\n",
        "    plt.bar(np.arange(len(x)) + x_offset, y, color=colors[i + 1], width=barWidth, label='Imitation 10 epochs' if i == 0 else 'Imitation 15 epochs' if i == 1 else 'Imitation 20 epochs')\n",
        "    \n",
        "plt.xlabel('Maximal Stage Reached', size=20, fontproperties=font_prop)\n",
        "plt.ylabel('Amount of episodes', size=20, fontproperties=font_prop)\n",
        "plt.suptitle('Amount of episodes reached each task stage', fontproperties=font_prop, size=25)\n",
        "plt.xticks(np.arange(len(x)) + 2*barWidth, ['no reward', 'log', 'planks', 'sticks', 'wooden pickaxe', 'cobblestone', 'stone pickaxe', 'iron ore', 'iron ingot', 'iron pickaxe'], fontproperties=font_prop, size=12)\n",
        "plt.yticks(np.arange(11.0), fontproperties=font_prop, size=12)\n",
        "plt.title('each stage contains all the previous stages', fontproperties=font_prop, size=20)\n",
        "plt.legend(prop=legend_font_prop)\n",
        "\n",
        "ax.set_facecolor('#EAEAF2')\n",
        "ax.grid(True)\n",
        "ax.set_axisbelow(True)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "lKUMgGJylitA",
        "outputId": "7cb46f86-3e83-4a9f-ab4e-fb392fe17c93"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
        "import matplotlib.colors as mcolors\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,8))\n",
        "# Data\n",
        "for i, agent in enumerate([imitation_agent_10, imitation_agent_15, imi_agent]):\n",
        "    x_values, y_values = [], []\n",
        "    cumulative = []\n",
        "    agent_reward_step = agent['reward_at']\n",
        "    for sublist in agent_reward_step:\n",
        "        if sublist:\n",
        "            scripted_reward_step_array = np.array(sublist).reshape(-1, 2)\n",
        "\n",
        "            # Calculate the cumulative sum of the second column\n",
        "            scripted_reward_step_array[:, 1] = np.cumsum(scripted_reward_step_array[:, 1])\n",
        "\n",
        "            cumulative.append(scripted_reward_step_array.tolist())\n",
        "\n",
        "            x_values.extend([item[0] for item in sublist])\n",
        "            y_values.extend([item[1] for item in sublist])\n",
        "\n",
        "    min_x = np.min(x_values)\n",
        "    max_x = np.max(x_values)\n",
        "    xy = []\n",
        "    for tier2 in cumulative:\n",
        "        filled_data = []\n",
        "        previous_x = None\n",
        "        for x, y in tier2:\n",
        "            if previous_x is None:\n",
        "                # Add missing x values before the first data point\n",
        "                if x > 0:\n",
        "                    filled_data.extend([[i, 0.0] for i in range(0, int(x))])\n",
        "            else:\n",
        "                # Add missing x values between the previous and current data point\n",
        "                if x - previous_x > 1:\n",
        "                    filled_data.extend([[i, previous_y] for i in range(int(previous_x) + 1, int(x))])\n",
        "\n",
        "            filled_data.append([x, y])\n",
        "            previous_x = x\n",
        "            previous_y = y\n",
        "\n",
        "        # Add missing x values after the last data point\n",
        "        max_xi = filled_data[-1][0]\n",
        "        filled_data.extend([[i, filled_data[-1][1]] for i in range(int(max_xi) + 1, max_x + 1)])\n",
        "        xy.append(filled_data)\n",
        "\n",
        "    x_values = range(max_x + 1)\n",
        "\n",
        "    y_values = [[sublist[idx][1] for idx in range(len(x_values))] for sublist in xy]\n",
        "    data_array = np.array(y_values, ndmin=2)\n",
        "\n",
        "\n",
        "    # Find the mean of each index\n",
        "    mean_of_each_index = np.mean(data_array, axis=0)\n",
        "\n",
        "    # Find the min of each index\n",
        "    min_of_each_index = np.min(data_array, axis=0)\n",
        "\n",
        "    # Find the max of each index\n",
        "    max_of_each_index = np.max(data_array, axis=0)\n",
        "\n",
        "\n",
        "    plt.plot(x_values, mean_of_each_index, color=colors[i], label='Imitation 10 epochs' if i == 0 else 'Imitation 15 epochs' if i == 1 else 'Imitation 20 epochs', lw = 2)\n",
        "    color_rgb = mcolors.hex2color(colors[i])  # Convert hexadecimal color to RGB tuple\n",
        "\n",
        "    # Adjust brightness by multiplying each RGB component\n",
        "    lighter_color_rgb = tuple(min(c * 1.2, 1.0) for c in color_rgb)\n",
        "\n",
        "    # Adjust opacity by setting a lower alpha value\n",
        "    lighter_color_rgba = lighter_color_rgb + (0.5,)  # Add an alpha value (e.g., 0.5 for semi-transparency)\n",
        "\n",
        "    # Convert the adjusted colors back to hexadecimal notation if needed\n",
        "    lighter_color_hex = mcolors.rgb2hex(lighter_color_rgb)\n",
        "    lighter_color_hex_with_alpha = mcolors.rgb2hex(lighter_color_rgba)\n",
        "    # Plot the shaded region between the min and max borders\n",
        "    plt.fill_between(x_values, min_of_each_index, max_of_each_index, color=lighter_color_hex_with_alpha, alpha=0.5, label='Min-Max ' + ('Imitation 10 epochs' if i == 0 else 'Imitation 15 epochs' if i == 1 else 'Imitation 20 epochs'))\n",
        "\n",
        "    # Customize the plot\n",
        "    plt.xlabel('Steps', fontproperties=font_prop, size=15)\n",
        "    plt.ylabel('Avg. Reward', fontproperties=font_prop, size=15)\n",
        "    plt.title('Average reward per step throughout learning', fontproperties=font_prop, size=20)\n",
        "    plt.legend(loc='upper left', prop=legend_font_prop)\n",
        "\n",
        "ax.set_facecolor('#EAEAF2')\n",
        "ax.grid(True)\n",
        "ax.set_axisbelow(True)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "id": "z6CGWLr4hrbc",
        "outputId": "84ab9de6-3ff1-4322-c1fd-a81d2222170d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "bc_agent_10 = load_json('/content/drive/MyDrive/minecraft proj/For comparison/basic_bc_10.json') # change these paths to your paths\n",
        "bc_agent_15 = load_json('/content/drive/MyDrive/minecraft proj/For comparison/basic_bc_15.json')\n",
        "\n",
        "# set width of bar\n",
        "barWidth = 0.25\n",
        "fig, ax = plt.subplots(figsize =(12, 8))\n",
        "\n",
        "# set height of bar\n",
        "bc_10_stats = [np.mean(bc_agent_10[i]) for i in ['episode', 'runtime','reward']]\n",
        "bc_15_stats = [np.mean(bc_agent_15[i]) for i in ['episode', 'runtime','reward']]\n",
        "bc_stats = [np.mean(bc_agent[i]) for i in ['episode', 'runtime','reward']]\n",
        "\n",
        "# Set position of bar on X axis\n",
        "br1 = np.arange(len(random_stats))\n",
        "br2 = [x + barWidth for x in br1]\n",
        "br3 = [x + barWidth for x in br2]\n",
        "\n",
        "# Make the plot\n",
        "plt.bar(br1, bc_10_stats, color=colors[0], width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='BC 10 epochs')\n",
        "plt.bar(br2, bc_15_stats, color=colors[1], width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='BC 15 epochs')\n",
        "plt.bar(br3, bc_stats, color =colors[2], width = barWidth,\n",
        "\t\tedgecolor ='grey', label ='BC 20 epochs')\n",
        "\n",
        "# Adding Xticks\n",
        "plt.title('General Measurements For Behavioral Cloning throughout the learning process', fontproperties=font_prop, size=15)\n",
        "\n",
        "plt.xlabel('Measurement Type', fontproperties=font_prop, size=12)\n",
        "plt.xticks([r + barWidth for r in range(len(random_stats))],\n",
        "\t\t['total episode', 'mean survival time', 'mean reward'], fontproperties=font_prop, size=10)\n",
        "\n",
        "plt.legend(prop=legend_font_prop)\n",
        "\n",
        "ax.set_facecolor('#EAEAF2')\n",
        "ax.grid(True)\n",
        "ax.set_axisbelow(True)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "id": "NiJ-K1ooh37g",
        "outputId": "d00c51ce-c321-44e9-87ca-cc021f88e8df"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "barWidth = 0.33\n",
        "x = np.array([0.0, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0, 256.0])\n",
        "fig, ax = plt.subplots(figsize=(20,10))\n",
        "for i, agent in enumerate([bc_agent_10, bc_agent_15, bc_agent]):\n",
        "    agent_reward_step = agent['reward_at']\n",
        "    c=Counter()\n",
        "    # Iterate over each sublist in the given data\n",
        "    for sublist in agent_reward_step:\n",
        "        if sublist:\n",
        "            c[sublist[-1][1]]+=1\n",
        "        \n",
        "    y=[c[i] for i in x if i != 0.0]\n",
        "    y.insert(0, len(agent_reward_step) - sum(y))\n",
        "    \n",
        "\n",
        "# Plot the bar chart with colors based on the normalized reward values\n",
        "    x_offset = i * barWidth\n",
        "    plt.bar(np.arange(len(x)) + x_offset, y, color=colors[i + 1], width=barWidth, label='BC 10 epochs' if i == 0 else 'BC 15 epochs' if i == 1 else 'BC 20 epochs')\n",
        "    \n",
        "plt.xlabel('Max. Stage Reached', size=20, fontproperties=font_prop)\n",
        "plt.ylabel('Amount of episodes', size=20, fontproperties=font_prop)\n",
        "plt.suptitle('Amount of episodes reached each task stage', fontproperties=font_prop, size=25)\n",
        "plt.xticks(np.arange(len(x)) + 2*barWidth, ['no reward', 'log', 'planks', 'sticks', 'wooden pickaxe', 'cobblestone', 'stone pickaxe', 'iron ore', 'iron ingot', 'iron pickaxe'], fontproperties=font_prop, size=12)\n",
        "plt.yticks(np.arange(11.0), fontproperties=font_prop, size=12)\n",
        "plt.title('each stage contains all the previous stages', fontproperties=font_prop, size=20)\n",
        "plt.legend(prop=legend_font_prop)\n",
        "\n",
        "ax.set_facecolor('#EAEAF2')\n",
        "ax.grid(True)\n",
        "ax.set_axisbelow(True)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "By9bfrjiiRuv",
        "outputId": "8063fd4e-194d-4261-b4d5-5fa2b1fe0815"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
        "import matplotlib.colors as mcolors\n",
        "\n",
        "fix, ax = plt.subplots(figsize=(12,8))\n",
        "# Data\n",
        "for i, agent in enumerate([bc_agent_10, bc_agent_15, bc_agent]):\n",
        "    x_values, y_values = [], []\n",
        "    cumulative = []\n",
        "    agent_reward_step = agent['reward_at']\n",
        "    for sublist in agent_reward_step:\n",
        "        if sublist:\n",
        "            scripted_reward_step_array = np.array(sublist).reshape(-1, 2)\n",
        "\n",
        "            # Calculate the cumulative sum of the second column\n",
        "            scripted_reward_step_array[:, 1] = np.cumsum(scripted_reward_step_array[:, 1])\n",
        "\n",
        "            cumulative.append(scripted_reward_step_array.tolist())\n",
        "\n",
        "            x_values.extend([item[0] for item in sublist])\n",
        "            y_values.extend([item[1] for item in sublist])\n",
        "\n",
        "    min_x = np.min(x_values)\n",
        "    max_x = np.max(x_values)\n",
        "    xy = []\n",
        "\n",
        "    for tier2 in cumulative:\n",
        "        filled_data = []\n",
        "        previous_x = None\n",
        "        for x, y in tier2:\n",
        "            if previous_x is None:\n",
        "                # Add missing x values before the first data point\n",
        "                if x > 0:\n",
        "                    filled_data.extend([[i, 0.0] for i in range(0, int(x))])\n",
        "            else:\n",
        "                # Add missing x values between the previous and current data point\n",
        "                if x - previous_x > 1:\n",
        "                    filled_data.extend([[i, previous_y] for i in range(int(previous_x) + 1, int(x))])\n",
        "\n",
        "            filled_data.append([x, y])\n",
        "            previous_x = x\n",
        "            previous_y = y\n",
        "\n",
        "        # Add missing x values after the last data point\n",
        "        max_xi = filled_data[-1][0]\n",
        "        filled_data.extend([[i, filled_data[-1][1]] for i in range(int(max_xi) + 1, max_x + 1)])\n",
        "        xy.append(filled_data)\n",
        "\n",
        "    x_values = range(max_x + 1)\n",
        "\n",
        "    y_values = [[sublist[idx][1] for idx in range(len(x_values))] for sublist in xy]\n",
        "    data_array = np.array(y_values, ndmin=2)\n",
        "\n",
        "\n",
        "    # Find the mean of each index\n",
        "    mean_of_each_index = np.mean(data_array, axis=0)\n",
        "\n",
        "    # Find the min of each index\n",
        "    min_of_each_index = np.min(data_array, axis=0)\n",
        "\n",
        "    # Find the max of each index\n",
        "    max_of_each_index = np.max(data_array, axis=0)\n",
        "\n",
        "\n",
        "    plt.plot(x_values, mean_of_each_index, color=colors[i+1], label='BC 10 epochs' if i == 0 else 'BC 15 epochs' if i == 1 else 'BC 20 epochs', lw = 2)\n",
        "    color_rgb = mcolors.hex2color(colors[i+1])  # Convert hexadecimal color to RGB tuple\n",
        "\n",
        "    # Adjust brightness by multiplying each RGB component\n",
        "    lighter_color_rgb = tuple(min(c * 1.2, 1.0) for c in color_rgb)\n",
        "\n",
        "    # Adjust opacity by setting a lower alpha value\n",
        "    lighter_color_rgba = lighter_color_rgb + (0.5,)  # Add an alpha value (e.g., 0.5 for semi-transparency)\n",
        "\n",
        "    # Convert the adjusted colors back to hexadecimal notation if needed\n",
        "    lighter_color_hex = mcolors.rgb2hex(lighter_color_rgb)\n",
        "    lighter_color_hex_with_alpha = mcolors.rgb2hex(lighter_color_rgba)\n",
        "    # Plot the shaded region between the min and max borders\n",
        "    plt.fill_between(x_values, min_of_each_index, max_of_each_index, color=lighter_color_hex_with_alpha, alpha=0.5, label='Min-Max ' + ('BC 10 epochs' if i == 0 else 'BC 15 epochs' if i == 1 else 'BC 20 epochs'))\n",
        "\n",
        "    plt.xlabel('Steps', fontproperties=font_prop, size=15)\n",
        "    plt.ylabel('Avg. Reward', fontproperties=font_prop, size=15)\n",
        "    plt.title('Average reward per step throughout learning', fontproperties=font_prop, size=20)\n",
        "    plt.legend(loc='upper right', prop=legend_font_prop)\n",
        "\n",
        "ax.set_facecolor('#EAEAF2')\n",
        "ax.grid(True)\n",
        "ax.set_axisbelow(True)\n",
        "\n",
        "plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}