03_cart_pole/notebooks/07_deep_q_agent_good_hyperparameters.ipynb

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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# 07 Deep Q agent with good hyper-parameters\n",
    "\n",
    "#### 👉 Let's use a neural network model to approximate the Q function.\n",
    "\n",
    "#### 👉Neural networks are usually highly-parametric models that are able to fit complex patterns between the input features and the target.\n",
    "\n",
    "#### 👉 The type of neural network we will use is a Multi Layer Perceptron (MLP).\n",
    "\n",
    "#### 👉MLPs are stacks of linear models, interleaved with so called activation functions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e53d094",
   "metadata": {},
   "source": [
    "![nn](https://github.com/Paulescu/hands-on-rl/blob/main/03_cart_pole/images/neural_net.jpg?raw=true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd7b5c91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%pylab inline\n",
    "%config InlineBackend.figure_format = 'svg'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d71e5be",
   "metadata": {},
   "source": [
    "## Environment 🌎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bfb033c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "env = gym.make('CartPole-v1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2e0469",
   "metadata": {},
   "source": [
    "## Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2cbf87c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Good hyper-parameters\n",
    "# make you feel great!\n",
    "hparams = {\n",
    "    'learning_rate': 0.00016151809562265122,\n",
    "    'discount_factor': 0.99,\n",
    "    'batch_size': 32,\n",
    "    'memory_size': 10000,\n",
    "    'freq_steps_train': 8,\n",
    "    'freq_steps_update_target': 10,\n",
    "    'n_steps_warm_up_memory': 1000,\n",
    "    'n_gradient_steps': 16,\n",
    "    'nn_hidden_layers': [256, 256],\n",
    "    'max_grad_norm': 10,\n",
    "    'normalize_state': False,\n",
    "    'epsilon_start': 0.9,\n",
    "    'epsilon_end': 0.14856584122699473,\n",
    "    'steps_epsilon_decay': 10000,\n",
    "}\n",
    "\n",
    "# Lapadula: New hparams from saved_agents/CartPole-v1/298/hparams.json\n",
    "hparams = {\"learning_rate\": 0.00045095481485457226, \"discount_factor\": 0.99, \"batch_size\": 16, \"memory_size\": 100000, \"freq_steps_update_target\": 10, \"n_steps_warm_up_memory\": 1000, \"freq_steps_train\": 8, \"n_gradient_steps\": 16, \"nn_hidden_layers\": [256, 256], \"max_grad_norm\": 1, \"normalize_state\": False, \"epsilon_start\": 0.9, \"epsilon_end\": 0.06286625175600052, \"steps_epsilon_decay\": 10000}\n",
    "\n",
    "SEED = 2386916045"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f4fdb9",
   "metadata": {},
   "source": [
    "## ⚠️ Fix random seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dc03521c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.utils import set_seed\n",
    "set_seed(env, SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abdcf0ea",
   "metadata": {},
   "source": [
    "## Deep Q-Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9ac5f1e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67,586 parameters\n"
     ]
    }
   ],
   "source": [
    "from src.q_agent import QAgent\n",
    "agent = QAgent(env, **hparams)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f31c94bc",
   "metadata": {},
   "source": [
    "## Train the agent 🏋️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "779c0ad2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reward mean: 500.00, std: 0.00\n",
      "Num steps mean: 500.00, std: 0.00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from src.loops import train\n",
    "train(agent, env, n_episodes=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c9f72a",
   "metadata": {},
   "source": [
    "## Evaluate the agent ⏱️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ec4d363b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 1000/1000 [01:42<00:00,  9.74it/s]\n"
     ]
    }
   ],
   "source": [
    "from src.loops import evaluate\n",
    "rewards, steps = evaluate(\n",
    "    agent, env,\n",
    "    n_episodes=1000,\n",
    "    epsilon=0.00\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8dedf1ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reward average 500.00, std 0.00\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "reward_avg = np.array(rewards).mean()\n",
    "reward_std = np.array(rewards).std()\n",
    "print(f'Reward average {reward_avg:.2f}, std {reward_std:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f76f648d",
   "metadata": {},
   "source": [
    "## Let's see how far we got in each attempt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f349b9a6",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (10, 4))\n",
    "ax.set_title(\"Rewards\")    \n",
    "pd.Series(rewards).plot(kind='hist', bins=100)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcbf6d1d",
   "metadata": {},
   "source": [
    "## Let's see our agent in action 🎬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "de569e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Workaround for pygame error: \"error: No available video device\"\n",
    "# See https://stackoverflow.com/questions/15933493/pygame-error-no-available-video-device?rq=1\n",
    "# This is probably needed only for Linux\n",
    "import os\n",
    "os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\n",
    "\n",
    "from src.viz import show_video\n",
    "show_video(agent, env, sleep_sec=0.01, seed=123)"
   ]
  }
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