#!/usr/bin/env python # coding=utf-8 ''' @Author: John @Email: johnjim0816@gmail.com @Date: 2020-06-12 00:48:57 @LastEditor: John LastEditTime: 2021-01-05 09:41:02 @Discription: @Environment: python 3.7.7 ''' import gym import torch from agent import DQN import argparse from torch.utils.tensorboard import SummaryWriter import datetime import os from utils import save_results,save_model SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/result/"+SEQUENCE+'/' def get_args(): '''模型参数 ''' parser = argparse.ArgumentParser() parser.add_argument("--train", default=1, type=int) # 1 表示训练，0表示只进行eval parser.add_argument("--gamma", default=0.99, type=float) # q-learning中的gamma parser.add_argument("--epsilon_start", default=0.95, type=float) # 基于贪心选择action对应的参数epsilon parser.add_argument("--epsilon_end", default=0.01, type=float) parser.add_argument("--epsilon_decay", default=500, type=float) parser.add_argument("--policy_lr", default=0.01, type=float) parser.add_argument("--memory_capacity", default=1000, type=int, help="capacity of Replay Memory") parser.add_argument("--batch_size", default=32, type=int, help="batch size of memory sampling") parser.add_argument("--train_eps", default=200, type=int) # 训练的最大episode数目 parser.add_argument("--train_steps", default=200, type=int) parser.add_argument("--target_update", default=2, type=int, help="when(every default 2 eisodes) to update target net ") # 更新频率 parser.add_argument("--eval_eps", default=100, type=int) # 训练的最大episode数目 parser.add_argument("--eval_steps", default=200, type=int) # 训练每个episode的长度 config = parser.parse_args() return config def train(cfg): print('Start to train ! \n') device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu env = gym.make('CartPole-v0') env.seed(1) # 设置env随机种子 n_states = env.observation_space.shape[0] n_actions = env.action_space.n agent = DQN(n_states=n_states, n_actions=n_actions, device=device, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start, epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay, policy_lr=cfg.policy_lr, memory_capacity=cfg.memory_capacity, batch_size=cfg.batch_size) rewards = [] moving_average_rewards = [] ep_steps = [] log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/train/" + SEQUENCE writer = SummaryWriter(log_dir) for i_episode in range(1, cfg.train_eps+1): state = env.reset() # reset环境状态 ep_reward = 0 for i_step in range(1, cfg.train_steps+1): action = agent.choose_action(state) # 根据当前环境state选择action next_state, reward, done, _ = env.step(action) # 更新环境参数 ep_reward += reward agent.memory.push(state, action, reward, next_state, done) # 将state等这些transition存入memory state = next_state # 跳转到下一个状态 agent.update() # 每步更新网络 if done: break # 更新target network，复制DQN中的所有weights and biases if i_episode % cfg.target_update == 0: agent.target_net.load_state_dict(agent.policy_net.state_dict()) print('Episode:', i_episode, ' Reward: %i' % int(ep_reward), 'n_steps:', i_step, 'done: ', done,' Explore: %.2f' % agent.epsilon) ep_steps.append(i_step) rewards.append(ep_reward) # 计算滑动窗口的reward if i_episode == 1: moving_average_rewards.append(ep_reward) else: moving_average_rewards.append( 0.9*moving_average_rewards[-1]+0.1*ep_reward) writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode) writer.add_scalar('steps_of_each_episode', ep_steps[-1], i_episode) writer.close() print('Complete training！') ''' 保存模型 ''' save_model(agent,model_path=SAVED_MODEL_PATH) '''存储reward等相关结果''' save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path=RESULT_PATH) def eval(cfg, saved_model_path = SAVED_MODEL_PATH): print('start to eval ! \n') device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要 env.seed(1) # 设置env随机种子 n_states = env.observation_space.shape[0] n_actions = env.action_space.n agent = DQN(n_states=n_states, n_actions=n_actions, device="cpu", gamma=cfg.gamma, epsilon_start=cfg.epsilon_start, epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay, policy_lr=cfg.policy_lr, memory_capacity=cfg.memory_capacity, batch_size=cfg.batch_size) agent.load_model(saved_model_path+'checkpoint.pth') rewards = [] moving_average_rewards = [] ep_steps = [] log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/eval/" + SEQUENCE writer = SummaryWriter(log_dir) for i_episode in range(1, cfg.eval_eps+1): state = env.reset() # reset环境状态 ep_reward = 0 for i_step in range(1, cfg.eval_steps+1): action = agent.choose_action(state,train=False) # 根据当前环境state选择action next_state, reward, done, _ = env.step(action) # 更新环境参数 ep_reward += reward state = next_state # 跳转到下一个状态 if done: break print('Episode:', i_episode, ' Reward: %i' % int(ep_reward), 'n_steps:', i_step, 'done: ', done) ep_steps.append(i_step) rewards.append(ep_reward) # 计算滑动窗口的reward if i_episode == 1: moving_average_rewards.append(ep_reward) else: moving_average_rewards.append( 0.9*moving_average_rewards[-1]+0.1*ep_reward) writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode) writer.add_scalar('steps_of_each_episode', ep_steps[-1], i_episode) writer.close() '''存储reward等相关结果''' save_results(rewards,moving_average_rewards,ep_steps,tag='eval',result_path=RESULT_PATH) print('Complete evaling！') if __name__ == "__main__": cfg = get_args() if cfg.train: train(cfg) eval(cfg) else: model_path = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/" eval(cfg,saved_model_path=model_path)