129 lines
4.6 KiB
Python
129 lines
4.6 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: 2021-09-15 02:19:54
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@Discription:
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@Environment: python 3.7.7
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'''
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import sys,os
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curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
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parent_path = os.path.dirname(curr_path) # 父路径
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sys.path.append(parent_path) # 添加父路径到系统路径sys.path
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import gym
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import torch
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import datetime
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from common.utils import save_results, make_dir
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from common.plot import plot_rewards
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from DQN.agent import DQN
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
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class DQNConfig:
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def __init__(self):
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self.algo = "DQN" # 算法名称
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self.env = 'CartPole-v0' # 环境名称
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self.result_path = curr_path+"/outputs/" + self.env + \
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'/'+curr_time+'/results/' # path to save results
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self.model_path = curr_path+"/outputs/" + self.env + \
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'/'+curr_time+'/models/' # path to save models
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self.train_eps = 200 # 训练的回合数
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self.eval_eps = 30 # 测试的回合数
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self.gamma = 0.95
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self.epsilon_start = 0.90 # e-greedy策略中初始epsilon
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self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
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self.epsilon_decay = 500 # e-greedy策略中epsilon的衰减率
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self.lr = 0.0001 # 学习率
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self.memory_capacity = 100000 # capacity of Replay Memory
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self.batch_size = 64
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self.target_update = 4 # 目标网络的更新频率
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self.device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu") # jian che
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self.hidden_dim = 256 # hidden size of net
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def env_agent_config(cfg,seed=1):
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env = gym.make(cfg.env)
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env.seed(seed)
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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_actions,cfg)
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return env,agent
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def train(cfg, env, agent):
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print('Start to train !')
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print(f'Env: {cfg.env}, Algorithm: {cfg.algo}, Device: {cfg.device}')
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rewards = []
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ma_rewards = [] # moveing average reward
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for i_ep in range(cfg.train_eps):
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state = env.reset()
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done = False
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ep_reward = 0
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while True:
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action = agent.choose_action(state)
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next_state, reward, done, _ = env.step(action)
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ep_reward += reward
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agent.memory.push(state, action, reward, next_state, done)
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state = next_state
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agent.update()
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if done:
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break
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if (i_ep+1) % cfg.target_update == 0:
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agent.target_net.load_state_dict(agent.policy_net.state_dict())
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if (i_ep+1)%10 == 0:
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print('Episode:{}/{}, Reward:{}'.format(i_ep+1, cfg.train_eps, ep_reward))
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rewards.append(ep_reward)
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# save ma_rewards
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if ma_rewards:
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ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
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else:
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ma_rewards.append(ep_reward)
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print('Complete training！')
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return rewards, ma_rewards
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def eval(cfg,env,agent):
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print('Start to eval !')
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print(f'Env: {cfg.env}, Algorithm: {cfg.algo}, Device: {cfg.device}')
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rewards = []
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ma_rewards = [] # moving average rewards
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for i_ep in range(cfg.eval_eps):
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ep_reward = 0 # reward per episode
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state = env.reset()
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while True:
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action = agent.predict(state)
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next_state, reward, done, _ = env.step(action)
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state = next_state
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ep_reward += reward
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if done:
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break
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rewards.append(ep_reward)
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if ma_rewards:
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ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
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else:
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ma_rewards.append(ep_reward)
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print(f"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
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print('Complete evaling！')
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return rewards,ma_rewards
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if __name__ == "__main__":
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cfg = DQNConfig()
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# train
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env,agent = env_agent_config(cfg,seed=1)
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rewards, ma_rewards = train(cfg, env, agent)
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make_dir(cfg.result_path, cfg.model_path)
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agent.save(path=cfg.model_path)
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save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
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plot_rewards(rewards, ma_rewards, tag="train",
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algo=cfg.algo, path=cfg.result_path)
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# eval
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env,agent = env_agent_config(cfg,seed=10)
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agent.load(path=cfg.model_path)
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rewards,ma_rewards = eval(cfg,env,agent)
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save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
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plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
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