94 lines
4.0 KiB
Python
94 lines
4.0 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: 2021-03-26 17:17:17
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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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sys.path.append(os.getcwd()) # 添加当前终端路径
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import gym
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import torch
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import datetime
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from DQN.agent import DQN
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from common.plot import plot_rewards
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from common.utils import save_results
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SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
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SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
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if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): # 检测是否存在文件夹
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os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
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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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RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
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if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): # 检测是否存在文件夹
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os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
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if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
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os.mkdir(RESULT_PATH)
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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.gamma = 0.99
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self.epsilon_start = 0.95 # e-greedy策略的初始epsilon
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self.epsilon_end = 0.01
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self.epsilon_decay = 200
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self.lr = 0.01 # 学习率
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self.memory_capacity = 800 # Replay Memory容量
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self.batch_size = 64
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self.train_eps = 300 # 训练的episode数目
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self.train_steps = 200 # 训练每个episode的最大长度
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self.target_update = 2 # target net的更新频率
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self.eval_eps = 20 # 测试的episode数目
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self.eval_steps = 200 # 测试每个episode的最大长度
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
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self.hidden_dim = 128 # 神经网络隐藏层维度
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def train(cfg,env,agent):
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print('Start to train !')
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rewards = []
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ma_rewards = [] # 滑动平均的reward
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ep_steps = []
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for i_episode in range(cfg.train_eps):
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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(cfg.train_steps):
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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:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step+1,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 ma_rewards:
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ma_rewards.append(
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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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if __name__ == "__main__":
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cfg = DQNConfig()
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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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state_dim = env.observation_space.shape[0]
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action_dim = env.action_space.n
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agent = DQN(state_dim,action_dim,cfg)
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rewards,ma_rewards = train(cfg,env,agent)
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agent.save(path=SAVED_MODEL_PATH)
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save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
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plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)
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