157 lines
7.1 KiB
Python
157 lines
7.1 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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@Author: John
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@Email: johnjim0816@gmail.com
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@Date: 2020-06-12 00:48:57
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@LastEditor: John
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LastEditTime: 2020-10-15 22:00:28
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@Discription:
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@Environment: python 3.7.7
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'''
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import gym
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import torch
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from agent import DQN
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import argparse
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from torch.utils.tensorboard import SummaryWriter
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import datetime
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import os
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from utils import save_results
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SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
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SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/'
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RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/result/"+SEQUENCE+'/'
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def get_args():
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'''模型参数
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'''
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parser = argparse.ArgumentParser()
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parser.add_argument("--train", default=1, type=int) # 1 表示训练，0表示只进行eval
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parser.add_argument("--gamma", default=0.99,
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type=float) # q-learning中的gamma
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parser.add_argument("--epsilon_start", default=0.95,
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type=float) # 基于贪心选择action对应的参数epsilon
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parser.add_argument("--epsilon_end", default=0.01, type=float)
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parser.add_argument("--epsilon_decay", default=500, type=float)
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parser.add_argument("--policy_lr", default=0.01, type=float)
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parser.add_argument("--memory_capacity", default=1000,
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type=int, help="capacity of Replay Memory")
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parser.add_argument("--batch_size", default=32, type=int,
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help="batch size of memory sampling")
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parser.add_argument("--train_eps", default=200, type=int) # 训练的最大episode数目
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parser.add_argument("--train_steps", default=200, type=int)
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parser.add_argument("--target_update", default=2, type=int,
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help="when(every default 2 eisodes) to update target net ") # 更新频率
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parser.add_argument("--eval_eps", default=100, type=int) # 训练的最大episode数目
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parser.add_argument("--eval_steps", default=200,
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type=int) # 训练每个episode的长度
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config = parser.parse_args()
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return config
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def train(cfg):
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print('Start to train ! \n')
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
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env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要
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env.seed(1) # 设置env随机种子
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n_states = env.observation_space.shape[0]
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n_actions = env.action_space.n
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agent = DQN(n_states=n_states, n_actions=n_actions, device=device, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
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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)
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rewards = []
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moving_average_rewards = []
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ep_steps = []
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log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/train/" + SEQUENCE
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writer = SummaryWriter(log_dir)
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for i_episode in range(1, cfg.train_eps+1):
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state = env.reset() # reset环境状态
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ep_reward = 0
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for i_step in range(1, cfg.train_steps+1):
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action = agent.choose_action(state) # 根据当前环境state选择action
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next_state, reward, done, _ = env.step(action) # 更新环境参数
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ep_reward += reward
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agent.memory.push(state, action, reward, next_state, done) # 将state等这些transition存入memory
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state = next_state # 跳转到下一个状态
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agent.update() # 每步更新网络
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if done:
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break
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# 更新target network，复制DQN中的所有weights and biases
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if i_episode % cfg.target_update == 0:
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agent.target_net.load_state_dict(agent.policy_net.state_dict())
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print('Episode:', i_episode, ' Reward: %i' %
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int(ep_reward), 'n_steps:', i_step, 'done: ', done,' Explore: %.2f' % agent.epsilon)
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ep_steps.append(i_step)
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rewards.append(ep_reward)
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# 计算滑动窗口的reward
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if i_episode == 1:
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moving_average_rewards.append(ep_reward)
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else:
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moving_average_rewards.append(
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0.9*moving_average_rewards[-1]+0.1*ep_reward)
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writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
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writer.add_scalar('steps_of_each_episode',
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ep_steps[-1], i_episode)
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writer.close()
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print('Complete training！')
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''' 保存模型 '''
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if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
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os.mkdir(SAVED_MODEL_PATH)
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agent.save_model(SAVED_MODEL_PATH+'checkpoint.pth')
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print('model saved！')
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'''存储reward等相关结果'''
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save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path=RESULT_PATH)
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def eval(cfg, saved_model_path = SAVED_MODEL_PATH):
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print('start to eval ! \n')
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
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env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要
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env.seed(1) # 设置env随机种子
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n_states = env.observation_space.shape[0]
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n_actions = env.action_space.n
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agent = DQN(n_states=n_states, n_actions=n_actions, device=device, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
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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)
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agent.load_model(saved_model_path+'checkpoint.pth')
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rewards = []
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moving_average_rewards = []
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ep_steps = []
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log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/eval/" + SEQUENCE
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writer = SummaryWriter(log_dir)
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for i_episode in range(1, cfg.eval_eps+1):
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state = env.reset() # reset环境状态
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ep_reward = 0
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for i_step in range(1, cfg.eval_steps+1):
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action = agent.choose_action(state,train=False) # 根据当前环境state选择action
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next_state, reward, done, _ = env.step(action) # 更新环境参数
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ep_reward += reward
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state = next_state # 跳转到下一个状态
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if done:
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break
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print('Episode:', i_episode, ' Reward: %i' %
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int(ep_reward), 'n_steps:', i_step, 'done: ', done)
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ep_steps.append(i_step)
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rewards.append(ep_reward)
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# 计算滑动窗口的reward
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if i_episode == 1:
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moving_average_rewards.append(ep_reward)
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else:
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moving_average_rewards.append(
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0.9*moving_average_rewards[-1]+0.1*ep_reward)
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writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
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writer.add_scalar('steps_of_each_episode',
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ep_steps[-1], i_episode)
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writer.close()
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'''存储reward等相关结果'''
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save_results(rewards,moving_average_rewards,ep_steps,tag='eval',result_path=RESULT_PATH)
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print('Complete evaling！')
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if __name__ == "__main__":
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cfg = get_args()
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if cfg.train:
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train(cfg)
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eval(cfg)
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else:
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model_path = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"
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eval(cfg,saved_model_path=model_path)
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