222 lines
14 KiB
Plaintext
222 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"from pathlib import Path\n",
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"curr_path = str(Path().absolute())\n",
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"parent_path = str(Path().absolute().parent)\n",
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"sys.path.append(parent_path) # add current terminal path to sys.path"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import gym\n",
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"import torch\n",
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"import datetime\n",
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"\n",
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"from SAC.env import NormalizedActions\n",
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"from SAC.agent import SAC\n",
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"from common.utils import save_results, make_dir\n",
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"from common.plot import plot_rewards\n",
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"\n",
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"curr_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") # obtain current time"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"class SACConfig:\n",
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" def __init__(self) -> None:\n",
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" self.algo = 'SAC'\n",
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" self.env = 'Pendulum-v0'\n",
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" self.result_path = curr_path+\"/outputs/\" +self.env+'/'+curr_time+'/results/' # path to save results\n",
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" self.model_path = curr_path+\"/outputs/\" +self.env+'/'+curr_time+'/models/' # path to save models\n",
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" self.train_eps = 300\n",
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" self.train_steps = 500\n",
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" self.test_eps = 50\n",
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" self.eval_steps = 500\n",
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" self.gamma = 0.99\n",
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" self.mean_lambda=1e-3\n",
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" self.std_lambda=1e-3\n",
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" self.z_lambda=0.0\n",
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" self.soft_tau=1e-2\n",
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" self.value_lr = 3e-4\n",
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" self.soft_q_lr = 3e-4\n",
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" self.policy_lr = 3e-4\n",
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" self.capacity = 1000000\n",
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" self.hidden_dim = 256\n",
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" self.batch_size = 128\n",
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" self.device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def env_agent_config(cfg,seed=1):\n",
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" env = NormalizedActions(gym.make(\"Pendulum-v0\"))\n",
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" env.seed(seed)\n",
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" action_dim = env.action_space.shape[0]\n",
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" state_dim = env.observation_space.shape[0]\n",
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" agent = SAC(state_dim,action_dim,cfg)\n",
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" return env,agent"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def train(cfg,env,agent):\n",
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" print('Start to train !')\n",
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" print(f'Env: {cfg.env}, Algorithm: {cfg.algo}, Device: {cfg.device}')\n",
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" rewards = []\n",
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" ma_rewards = [] # moveing average reward\n",
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" for i_ep in range(cfg.train_eps):\n",
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" state = env.reset()\n",
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" ep_reward = 0\n",
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" for i_step in range(cfg.train_steps):\n",
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" action = agent.policy_net.get_action(state)\n",
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" next_state, reward, done, _ = env.step(action)\n",
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" agent.memory.push(state, action, reward, next_state, done)\n",
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" agent.update()\n",
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" state = next_state\n",
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" ep_reward += reward\n",
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" if done:\n",
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" break\n",
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" if (i_ep+1)%10==0:\n",
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" print(f\"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}\")\n",
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" rewards.append(ep_reward)\n",
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" if ma_rewards:\n",
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" ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)\n",
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" else:\n",
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" ma_rewards.append(ep_reward) \n",
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" print('Complete training！')\n",
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" return rewards, ma_rewards"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def eval(cfg,env,agent):\n",
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" print('Start to eval !')\n",
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" print(f'Env: {cfg.env}, Algorithm: {cfg.algo}, Device: {cfg.device}')\n",
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" rewards = []\n",
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" ma_rewards = [] # moveing average reward\n",
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" for i_ep in range(cfg.test_eps):\n",
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" state = env.reset()\n",
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" ep_reward = 0\n",
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" for i_step in range(cfg.eval_steps):\n",
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" action = agent.policy_net.get_action(state)\n",
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" next_state, reward, done, _ = env.step(action)\n",
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" state = next_state\n",
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" ep_reward += reward\n",
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" if done:\n",
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" break\n",
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" if (i_ep+1)%10==0:\n",
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" print(f\"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}\")\n",
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" rewards.append(ep_reward)\n",
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" if ma_rewards:\n",
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" ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)\n",
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" else:\n",
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" ma_rewards.append(ep_reward) \n",
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" print('Complete evaling！')\n",
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" return rewards, ma_rewards\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"ename": "DeprecatedEnv",
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"evalue": "Env Pendulum-v0 not found (valid versions include ['Pendulum-v1'])",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/gym/envs/registration.py\u001b[0m in \u001b[0;36mspec\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv_specs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 159\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mKeyError\u001b[0m: 'Pendulum-v0'",
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"\nDuring handling of the above exception, another exception occurred:\n",
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"\u001b[0;31mDeprecatedEnv\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-7-91b1038013e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0magent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv_agent_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma_rewards\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mmake_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m<ipython-input-4-040773221550>\u001b[0m in \u001b[0;36menv_agent_config\u001b[0;34m(cfg, seed)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0menv_agent_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0menv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNormalizedActions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgym\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Pendulum-v0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0maction_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mstate_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobservation_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/gym/envs/registration.py\u001b[0m in \u001b[0;36mmake\u001b[0;34m(id, **kwargs)\u001b[0m\n\u001b[1;32m 233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mregistry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/gym/envs/registration.py\u001b[0m in \u001b[0;36mmake\u001b[0;34m(self, path, **kwargs)\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Making new env: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m \u001b[0mspec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 129\u001b[0m \u001b[0menv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/gym/envs/registration.py\u001b[0m in \u001b[0;36mspec\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m 185\u001b[0m raise error.DeprecatedEnv(\n\u001b[1;32m 186\u001b[0m \"Env {} not found (valid versions include {})\".format(\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatching_envs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m )\n\u001b[1;32m 189\u001b[0m )\n",
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"\u001b[0;31mDeprecatedEnv\u001b[0m: Env Pendulum-v0 not found (valid versions include ['Pendulum-v1'])"
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]
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}
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],
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"source": [
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"if __name__ == \"__main__\":\n",
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" cfg=SACConfig()\n",
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" \n",
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" # train\n",
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" env,agent = env_agent_config(cfg,seed=1)\n",
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" rewards, ma_rewards = train(cfg, env, agent)\n",
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" make_dir(cfg.result_path, cfg.model_path)\n",
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" agent.save(path=cfg.model_path)\n",
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" save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)\n",
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" plot_rewards(rewards, ma_rewards, tag=\"train\",\n",
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" algo=cfg.algo, path=cfg.result_path)\n",
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" # eval\n",
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" env,agent = env_agent_config(cfg,seed=10)\n",
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" agent.load(path=cfg.model_path)\n",
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" rewards,ma_rewards = eval(cfg,env,agent)\n",
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" save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)\n",
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" plot_rewards(rewards,ma_rewards,tag=\"eval\",env=cfg.env,algo = cfg.algo,path=cfg.result_path)\n"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "fe38df673a99c62a9fea33a7aceda74c9b65b12ee9d076c5851d98b692a4989a"
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},
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"kernelspec": {
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"display_name": "Python 3.7.10 64-bit ('mujoco': conda)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
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"version": "3.7.10"
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},
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"metadata": {
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||
"interpreter": {
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"hash": "fd81e6a9e450d5c245c1a0b5da0b03c89c450f614a13afa2acb1654375922756"
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||
}
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},
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"orig_nbformat": 2
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},
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||
"nbformat": 4,
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||
"nbformat_minor": 2
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||
}
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