examples/dynaArchitecture_planningAgents.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import gymnasium as gym\n",
    "from gymnasium.envs.toy_text.frozen_lake import generate_random_map\n",
    "\n",
    "from rlforge.agents import SarsaAgent, QAgent, ExpectedSarsaAgent\n",
    "from rlforge.environments import DynaMaze, ShortcutMaze\n",
    "from rlforge.experiments import runEpisodicExperiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = DynaMaze()\n",
    "expected_sarsa_agent = ExpectedSarsaAgent(step_size=0.5,\n",
    "                                          discount=0.95,\n",
    "                                          num_states=env.observation_space.n,\n",
    "                                          num_actions=env.action_space.n,\n",
    "                                          epsilon=0.1)\n",
    "dyna_q_agent = QAgent(step_size=0.5,\n",
    "                      discount=0.95,\n",
    "                      num_states=env.observation_space.n,\n",
    "                      num_actions=env.action_space.n,\n",
    "                      epsilon=0.1,\n",
    "                      planning=True,\n",
    "                      planning_steps=10,\n",
    "                      exploration_bonus=0)\n",
    "dyna_q_plus_agent = QAgent(step_size=0.5,\n",
    "                           discount=0.95,\n",
    "                           num_states=env.observation_space.n,\n",
    "                           num_actions=env.action_space.n,\n",
    "                           epsilon=0.1,\n",
    "                           planning=True,\n",
    "                           planning_steps=10,\n",
    "                           exploration_bonus=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                      \r"
     ]
    }
   ],
   "source": [
    "algorithms = [\"Expected SARSA\", \"DynaQ\", \"DynaQ+\"]\n",
    "agents = [expected_sarsa_agent, dyna_q_agent, dyna_q_plus_agent]\n",
    "\n",
    "rewards_list = []\n",
    "steps_per_episode_list = []\n",
    "\n",
    "for agent in agents:\n",
    "\n",
    "    rewards, steps_per_episode = runEpisodicExperiment(env, agent, 30, 50, 10000)\n",
    "\n",
    "    rewards_list.append(rewards)\n",
    "    steps_per_episode_list.append(steps_per_episode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx, algo_steps in enumerate(steps_per_episode_list):\n",
    "\n",
    "  plt.plot(np.mean(algo_steps, axis=1), label = algorithms[idx])\n",
    "\n",
    "plt.xlabel(\"Episode\")\n",
    "plt.ylabel(\"Average Steps per Episodes\")\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = ShortcutMaze(shortcut_episodes=20)\n",
    "dyna_q_agent = QAgent(step_size=0.5,\n",
    "                      discount=0.95,\n",
    "                      num_states=env.observation_space.n,\n",
    "                      num_actions=env.action_space.n,\n",
    "                      epsilon=0.1,\n",
    "                      planning=True,\n",
    "                      planning_steps=10,\n",
    "                      exploration_bonus=0)\n",
    "dyna_q_plus_agent = QAgent(step_size=0.5,\n",
    "                           discount=0.95,\n",
    "                           num_states=env.observation_space.n,\n",
    "                           num_actions=env.action_space.n,\n",
    "                           epsilon=0.1,\n",
    "                           planning=True,\n",
    "                           planning_steps=10,\n",
    "                           exploration_bonus=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                      \r"
     ]
    }
   ],
   "source": [
    "algorithms = [\"DynaQ\", \"DynaQ+\"]\n",
    "agents = [dyna_q_agent, dyna_q_plus_agent]\n",
    "\n",
    "rewards_list = []\n",
    "steps_per_episode_list = []\n",
    "\n",
    "for agent in agents:\n",
    "\n",
    "    rewards, steps_per_episode = runEpisodicExperiment(env, agent, 30, 50, 10000)\n",
    "\n",
    "    rewards_list.append(rewards)\n",
    "    steps_per_episode_list.append(steps_per_episode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx, algo_steps in enumerate(steps_per_episode_list):\n",
    "\n",
    "  plt.plot(np.mean(algo_steps, axis=1), label = algorithms[idx])\n",
    "\n",
    "plt.xlabel(\"Episode\")\n",
    "plt.ylabel(\"Average Steps per Episodes\")\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  }
 ],
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