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OpenAI Gym - Classic Control BM.ipynb
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openai-gym-classic-control-bm.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.4"
},
"colab": {
"name": "OpenAI Gym - Classic Control BM.ipynb",
"provenance": [],
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/analyticsindiamagazine/21cceac649a450c8b29888ee80c2a5af/openai-gym-classic-control-bm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PlJhNeqXlZxw",
"colab_type": "code",
"colab": {}
},
"source": [
"import gym\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.style.use('fivethirtyeight')\n",
"\n",
"import pandas as pd\n",
"import imageio\n",
"import time\n",
"import numpy as np\n",
"import gym\n",
"from stable_baselines.common.vec_env import DummyVecEnv, VecVideoRecorder, SubprocVecEnv\n",
"from stable_baselines.ddpg.policies import CnnPolicy, MlpPolicy\n",
"from stable_baselines.common.policies import MlpLstmPolicy, CnnLstmPolicy, MlpPolicy\n",
"from stable_baselines import A2C, PPO2, SAC, TD3, TRPO, DDPG, ACER, ACKTR, SAC\n",
"from stable_baselines.common.evaluation import evaluate_policy\n",
"from stable_baselines.common import set_global_seeds\n",
"\n",
"from stable_baselines.bench import Monitor\n",
"from stable_baselines.results_plotter import load_results, ts2xy"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Vl4-NA1alZx2",
"colab_type": "code",
"colab": {},
"outputId": "5572d76d-a09a-4b0f-eb56-65bf49d994f9"
},
"source": [
"plt.style.available"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['seaborn-dark',\n",
" 'seaborn-darkgrid',\n",
" 'seaborn-ticks',\n",
" 'fivethirtyeight',\n",
" 'seaborn-whitegrid',\n",
" 'classic',\n",
" '_classic_test',\n",
" 'fast',\n",
" 'seaborn-talk',\n",
" 'seaborn-dark-palette',\n",
" 'seaborn-bright',\n",
" 'seaborn-pastel',\n",
" 'grayscale',\n",
" 'seaborn-notebook',\n",
" 'ggplot',\n",
" 'seaborn-colorblind',\n",
" 'seaborn-muted',\n",
" 'seaborn',\n",
" 'Solarize_Light2',\n",
" 'seaborn-paper',\n",
" 'bmh',\n",
" 'tableau-colorblind10',\n",
" 'seaborn-white',\n",
" 'dark_background',\n",
" 'seaborn-poster',\n",
" 'seaborn-deep']"
]
},
"metadata": {
"tags": []
},
"execution_count": 61
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1IFI9-VWlZx5",
"colab_type": "code",
"colab": {},
"outputId": "11ab22f5-9ee7-4ba4-b850-64bd7fd58ccc"
},
"source": [
"gym.__version__"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'0.15.3'"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "HnakU1vdlZx9",
"colab_type": "code",
"colab": {}
},
"source": [
"def make_env(env_id, rank, seed=0):\n",
" \"\"\"\n",
" Utility function for multiprocessed env.\n",
" \n",
" :param env_id: (str) the environment ID\n",
" :param num_env: (int) the number of environment you wish to have in subprocesses\n",
" :param seed: (int) the inital seed for RNG\n",
" :param rank: (int) index of the subprocess\n",
" \"\"\"\n",
" def _init():\n",
" env = gym.make(env_id)\n",
" env.seed(seed + rank)\n",
" return env\n",
" set_global_seeds(seed)\n",
" return _init\n",
"\n",
"\n",
"\n",
"def evaluate(model, num_steps=1000):\n",
" \"\"\"\n",
" Evaluate a RL agent\n",
" :param model: (BaseRLModel object) the RL Agent\n",
" :param num_steps: (int) number of timesteps to evaluate it\n",
" :return: (float) Mean reward\n",
" \"\"\"\n",
" \n",
" episode_rewards = [[0.0] for _ in range(env.num_envs)]\n",
" obs = env.reset()\n",
" for i in range(num_steps):\n",
" # _states are only useful when using LSTM policies\n",
" actions, _states = model.predict(obs)\n",
" # here, action, rewards and dones are arrays\n",
" # because we are using vectorized env\n",
" obs, rewards, dones, info = env.step(actions)\n",
"\n",
" # Stats\n",
" for i in range(env.num_envs):\n",
" episode_rewards[i][-1] += rewards[i]\n",
" if dones[i]:\n",
" episode_rewards[i].append(0.0)\n",
"\n",
" mean_rewards = [0.0 for _ in range(env.num_envs)]\n",
" n_episodes = 0\n",
" for i in range(env.num_envs):\n",
" mean_rewards[i] = np.mean(episode_rewards[i]) \n",
" n_episodes += len(episode_rewards[i]) \n",
"\n",
" # Compute mean reward\n",
" mean_reward = round(np.mean(mean_rewards), 1)\n",
" print(\"Mean reward:\", mean_reward, \"Num episodes:\", n_episodes)\n",
"\n",
" return mean_reward"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PDW8hNq_lZyA",
"colab_type": "code",
"colab": {}
},
"source": [
"# Create a CNN based policy and optimize it using PPO2.\n",
"# ppo_params = {\"gamma\" : 0.99,\n",
"# \"n_steps\" : 128,\n",
"# \"ent_coef\" : 0.01,\n",
"# \"learning_rate\" : 0.00025,\n",
"# \"vf_coef\" : 0.5,\n",
"# \"max_grad_norm\" : 0.5,\n",
"# \"lam\" : 0.95,\n",
"# \"nminibatches\" : 4,\n",
"# \"noptepochs\" : 4,\n",
"# \"cliprange\" :0.2,\n",
"# \"cliprange_vf\" : None,\n",
"# \"verbose\" : 1,\n",
"# \"tensorboard_log\" : None,\n",
"# \"_init_setup_model\" : True,\n",
"# \"policy_kwargs\" : None,\n",
"# \"full_tensorboard_log\" : False,\n",
"# \"seed\" : None,\n",
"# \"n_cpu_tf_sess\" : None\n",
"# }\n",
"\n",
"# params=ppo_params, "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "reBxig4blZyD",
"colab_type": "code",
"colab": {}
},
"source": [
"def train_save_agent(model, env, gif_name, time_steps=int(1e4), \n",
" save_gif=False):\n",
" #use the policy, environment and define params to compile the PPO2 model..\n",
"# model = model #PPO2(\"MlpPolicy\", env)#, **params)\n",
"\n",
" s_time = time.time()\n",
" #Train the model\n",
" model.learn(total_timesteps=time_steps)\n",
" e_time = time.time()\n",
" tot_time = e_time - s_time\n",
" print(f\"Total Run-Time : , {tot_time : 0.3f} seconds\")\n",
"\n",
" if save_gif:\n",
" ########### Record-GIF ###########\n",
" images = []\n",
" obs = model.env.reset()\n",
" img = model.env.render(mode='rgb_array')\n",
" gif_length = 500\n",
"\n",
" for i in range(gif_length):\n",
" images.append(img)\n",
" action, _ = model.predict(obs)\n",
" obs, _, _ ,_ = model.env.step(action)\n",
" img = model.env.render(mode='rgb_array')\n",
"\n",
" imageio.mimsave(f'{gif_name}-{timesteps}.gif', [np.array(img) for i, img in enumerate(images) if i%2 == 0],\n",
" fps=29)\n",
" \n",
" return model, tot_time"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "z87sphMklZyG",
"colab_type": "code",
"colab": {},
"outputId": "f5fbb6d8-eb0f-4caf-89f6-b8c14ade38ba"
},
"source": [
"all_algs = [\"A2C\", \"PPO2\", \"ACER\", \"ACKTR\"]\n",
"\n",
"# Create log dir\n",
"import os\n",
"\n",
"log_dir = \"/tmp/gym/\"\n",
"os.makedirs(log_dir, exist_ok=True)\n",
"env_list = [\"Pendulum-v0\", \"MountainCar-v0\", \"Acrobot-v1\", \"CartPole-v1\"]\n",
"timesteps = int(1e6)\n",
"num_cpu = 4 # Number of processes to use\n",
"\n",
"\n",
"game_df = pd.DataFrame()\n",
"\n",
"for env_id in env_list:\n",
" print(f\"{env_id}......\")\n",
"# env = gym.make(env_id) #\n",
" # Logs will be saved in log_dir/monitor.csv\n",
"# env = Monitor(env, log_dir, allow_early_resets=True)\n",
"# env = DummyVecEnv([lambda: gym.make(env_id)])\n",
" env = SubprocVecEnv([make_env(env_id, i) for i in range(num_cpu)])\n",
" \n",
" alg_detail_df = pd.DataFrame()\n",
" for alg in all_algs:\n",
" if env_id == \"Pendulum-v0\" and alg == 'ACER':\n",
" print(f\"{env_id, alg}\")\n",
" pass\n",
" \n",
" else:\n",
" print(f'{alg}.....') \n",
" model = eval(alg + \"('MlpPolicy', env)\")\n",
" tr_model, run_time = train_save_agent(model, env, alg, time_steps=timesteps, save_gif=False)\n",
" # mean_reward, std_reward = evaluate_policy(tr_model, tr_model.get_env(), n_eval_episodes=20)\n",
" mean_reward = evaluate(tr_model, num_steps=1000)\n",
"\n",
" alg_detail_df = alg_detail_df.append([[env_id, alg, run_time, mean_reward]]) #, std_reward]])\n",
"\n",
" print(f\"Mean Reward : {mean_reward} \") #\"| Std_Reward : {std_reward}\")\n",
" \n",
" game_df = game_df.append(alg_detail_df) #], axis=1)\n",
" \n",
"game_df.columns = ['Envir', 'Algorithm', 'Run_Time', 'Mean_Rewards'] #, 'Std_Rewards'] "
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Pendulum-v0......\n",
"A2C.....\n",
"Total Run-Time : , 257.970 seconds\n",
"Mean reward: -551.4 Num episodes: 24\n",
"Mean Reward : -551.4 \n",
"PPO2.....\n",
"Total Run-Time : , 338.824 seconds\n",
"Mean reward: -939.9 Num episodes: 24\n",
"Mean Reward : -939.9 \n",
"('Pendulum-v0', 'ACER')\n",
"ACKTR.....\n",
"Total Run-Time : , 242.154 seconds\n",
"Mean reward: -876.8 Num episodes: 24\n",
"Mean Reward : -876.8 \n",
"MountainCar-v0......\n",
"A2C.....\n",
"Total Run-Time : , 237.251 seconds\n",
"Mean reward: -166.7 Num episodes: 24\n",
"Mean Reward : -166.7 \n",
"PPO2.....\n",
"Total Run-Time : , 690.426 seconds\n",
"Mean reward: -166.7 Num episodes: 24\n",
"Mean Reward : -166.7 \n",
"ACER.....\n",
"Total Run-Time : , 346.463 seconds\n",
"Mean reward: -166.7 Num episodes: 24\n",
"Mean Reward : -166.7 \n",
"ACKTR.....\n",
"Total Run-Time : , 234.560 seconds\n",
"Mean reward: -166.7 Num episodes: 24\n",
"Mean Reward : -166.7 \n",
"Acrobot-v1......\n",
"A2C.....\n",
"Total Run-Time : , 290.163 seconds\n",
"Mean reward: -89.6 Num episodes: 46\n",
"Mean Reward : -89.6 \n",
"PPO2.....\n",
"Total Run-Time : , 273.282 seconds\n",
"Mean reward: -74.9 Num episodes: 53\n",
"Mean Reward : -74.9 \n",
"ACER.....\n",
"Total Run-Time : , 392.469 seconds\n",
"Mean reward: -77.8 Num episodes: 51\n",
"Mean Reward : -77.8 \n",
"ACKTR.....\n",
"Total Run-Time : , 253.496 seconds\n",
"Mean reward: -72.3 Num episodes: 55\n",
"Mean Reward : -72.3 \n",
"CartPole-v1......\n",
"A2C.....\n",
"Total Run-Time : , 215.154 seconds\n",
"Mean reward: 333.3 Num episodes: 12\n",
"Mean Reward : 333.3 \n",
"PPO2.....\n",
"Total Run-Time : , 189.328 seconds\n",
"Mean reward: 333.3 Num episodes: 12\n",
"Mean Reward : 333.3 \n",
"ACER.....\n",
"Total Run-Time : , 300.396 seconds\n",
"Mean reward: 333.3 Num episodes: 12\n",
"Mean Reward : 333.3 \n",
"ACKTR.....\n",
"Total Run-Time : , 189.950 seconds\n",
"Mean reward: 333.3 Num episodes: 12\n",
"Mean Reward : 333.3 \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cBh97yTBlZyJ",
"colab_type": "code",
"colab": {}
},
"source": [
"# game_df.to_csv('Runtime_details.csv', index=False) "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Imn7Zmf6lZyM",
"colab_type": "code",
"colab": {},
"outputId": "c4e2070e-d007-4be7-f667-e3a9a29c6e5d"
},
"source": [
"alg_mean_rt = game_df.groupby(by='Algorithm', as_index=False)['Run_Time'].mean()\n",
"fig = plt.figure(dpi=130);\n",
"alg_mean_rt.set_index('Algorithm').sort_values(by='Run_Time', ascending=True).plot(kind='barh', \\\n",
" figsize=(14,6), color='red');\n",
"plt.title('Average Runtime of Various Algorithms across Enviroments (secs)', fontsize=13);\n",
"plt.xlabel('Time in secs');\n",
"plt.legend(loc='lower right', edgecolor='k');"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 780x520 with 0 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x432 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tPPbKr1olZyO",
"colab_type": "code",
"colab": {},
"outputId": "1aac4c18-ea2a-4398-c79a-37fbbcb8014b"
},
"source": [
"envir_df = game_df.groupby(by='Envir', as_index=False).mean()\n",
"envir_df.set_index('Envir', inplace=True)\n",
"fig = plt.figure(dpi=130);\n",
"envir_df['Run_Time'].sort_values().plot(kind='barh', figsize=(14,6), color='green');\n",
"plt.title('Average Runtime of Various Environments across Algorithms (secs)', fontsize=13);\n",
"plt.ylabel('Environment');\n",
"plt.xlabel('Time in secs');\n",
"plt.legend(loc='lower right', edgecolor='k');"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<Figure size 1820x780 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uwyNPwHNlZyT",
"colab_type": "code",
"colab": {},
"outputId": "132182a2-e725-4a98-9570-4b39f1affd46"
},
"source": [
"envir_df = game_df.groupby(by='Envir', as_index=False).mean()\n",
"envir_df.set_index('Envir', inplace=True)\n",
"fig = plt.figure(dpi=130);\n",
"envir_df['Mean_Rewards'].sort_values().plot(kind='barh', figsize=(14,6), color='orange');\n",
"plt.title('Average Rewards of Various Algorithms across Environments (secs)', fontsize=13);\n",
"plt.ylabel('Environment');\n",
"plt.xlabel('Average Rewards');\n",
"plt.legend(loc='lower right', edgecolor='k');"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1820x780 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3iWvj8_LlZyV",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YBSRmVpRlZyX",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nTa2uBOnlZya",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Nf7E3NDGlZyc",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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