check for gym v0.26 and update box2D installation

see openai/gym#100 (comment)

saving_loading_dqn.ipynb

@@ -1,24 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "saving_loading_dqn.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/saving_loading_dqn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -27,8 +13,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "hyyN-2qyK_T2",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "hyyN-2qyK_T2"
       },
       "source": [
         "# Stable Baselines3 - Training, Saving and Loading\n",
@@ -52,15 +38,14 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "gWskDE2c9WoN"
       },
+      "outputs": [],
       "source": [
-        "!apt install swig cmake\n",
-        "!pip install stable-baselines3[extra] box2d box2d-kengz"
-      ],
-      "execution_count": null,
-      "outputs": []
+        "!pip install stable-baselines3[extra] box2d-py"
+      ]
     },
     {
       "cell_type": "markdown",
@@ -73,17 +58,17 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "BIedd7Pz9sOs"
       },
+      "outputs": [],
       "source": [
         "import gym\n",
         "import numpy as np\n",
         "\n",
         "from stable_baselines3 import DQN"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -110,14 +95,14 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "pUWGZp3i9wyf"
       },
+      "outputs": [],
       "source": [
         "model = DQN('MlpPolicy', 'LunarLander-v2', verbose=1, exploration_final_eps=0.1, target_update_interval=250)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -130,14 +115,14 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "PeaVBGuJwK97"
       },
+      "outputs": [],
       "source": [
         "from stable_baselines3.common.evaluation import evaluate_policy"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -150,9 +135,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "xDHLMA6NFk95"
       },
+      "outputs": [],
       "source": [
         "# Separate env for evaluation\n",
         "eval_env = gym.make('LunarLander-v2')\n",
@@ -161,9 +148,7 @@
         "mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)\n",
         "\n",
         "print(f\"mean_reward={mean_reward:.2f} +/- {std_reward}\")"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -178,18 +163,18 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "e4cfSXIB-pTF"
       },
+      "outputs": [],
       "source": [
         "# Train the agent\n",
         "model.learn(total_timesteps=int(1e5))\n",
         "# Save the agent\n",
         "model.save(\"dqn_lunar\")\n",
         "del model  # delete trained model to demonstrate loading"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -202,39 +187,69 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "K1ExgtyZrIA6"
       },
+      "outputs": [],
       "source": [
         "model = DQN.load(\"dqn_lunar\")"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "ygl_gVmV_QP7"
       },
+      "outputs": [],
       "source": [
         "# Evaluate the trained agent\n",
         "mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)\n",
         "\n",
         "print(f\"mean_reward={mean_reward:.2f} +/- {std_reward}\")"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "aQDZI5VEGnUq"
       },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "name": "saving_loading_dqn.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3.9.13 ('sb3')",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.13"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "3201c96db5836b171d01fee72ea1be894646622d4b41771abf25c98b548a611d"
+      }
     }
-  ]
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }