Réorganiser le code pour définir la classe Agent et améliorer la structure du projet

M2/Reinforcement Learning/project/Project.ipynb

@@ -2,13 +2,16 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "7e37429a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import ale_py\n",
-    "import gymnasium as gym\n"
+    "import torch\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from pathlib import Path\n",
+    "\n"
    ]
   },
   {
@@ -16,35 +19,40 @@
    "execution_count": null,
    "id": "85ff0eb4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "A.L.E: Arcade Learning Environment (version 0.11.2+ecc1138)\n",
-      "[Powered by Stella]\n"
-     ]
-    },
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mLe noyau s’est bloqué lors de l’exécution du code dans une cellule active ou une cellule précédente. \n",
-      "\u001b[1;31mVeuillez vérifier le code dans la ou les cellules pour identifier une cause possible de l’échec. \n",
-      "\u001b[1;31mCliquez <a href='https://aka.ms/vscodeJupyterKernelCrash'>ici</a> pour plus d’informations. \n",
-      "\u001b[1;31mPour plus d’informations, consultez Jupyter <a href='command:jupyter.viewOutput'>log</a>."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "gym.register_envs(ale_py)\n",
+    "class Agent:\n",
+    "    \"\"\"Base class for reinforcement learning agents.\"\"\"\n",
     "\n",
-    "env = gym.make(\"ALE/Tennis-v5\", render_mode=\"human\")\n",
-    "obs, info = env.reset()\n",
-    "obs, reward, terminated, truncated, info = env.step(env.action_space.sample())\n",
-    "env.close()\n",
-    "\n"
+    "    def __init__(self, action_space: int) -> None:\n",
+    "        \"\"\"Initialize the agent.\"\"\"\n",
+    "        self.action_space = action_space\n",
+    "\n",
+    "    def get_action(self, observation: np.ndarray, epsilon: float = 0.0):\n",
+    "        \"\"\"Select an action based on the current observation.\"\"\"\n",
+    "        raise NotImplementedError\n",
+    "\n",
+    "    def update(self, state: np.ndarray, action: int, reward: float, next_state: np.ndarray, done: bool):\n",
+    "        \"\"\"Update the agent's knowledge based on the experience tuple.\"\"\"\n",
+    "        pass\n",
+    "\n",
+    "    def save(self, filename: str) -> None:\n",
+    "        \"\"\"Save the agent's state to a file.\"\"\"\n",
+    "        with Path(filename).open(\"wb\") as f:\n",
+    "          pickle.dump(self.__dict__, f)\n",
+    "\n",
+    "    def load(self, filename: str) -> None:\n",
+    "        \"\"\"Load the agent's state from a file.\"\"\"\n",
+    "        with Path(filename).open(\"rb\") as f:\n",
+    "          self.__dict__.update(pickle.load(f))  # noqa: S301\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2459be52",
+   "metadata": {},
+   "source": [
+    "## Random Agent"
    ]
   },
   {
@@ -53,12 +61,17 @@
    "id": "89633751",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "class RandomAgent(Agent):\n",
+    "  \"\"\"A simple agent that selects actions randomly.\"\"\"\n",
+    "  def get_action(self, observation, epsilon=0.0):\n",
+    "    return self.action_space.sample()\n"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "studies (3.13.9)",
    "language": "python",
    "name": "python3"
   },