92 lines
3.4 KiB
Python
92 lines
3.4 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: 2020-06-11 20:58:21
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@LastEditor: John
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LastEditTime: 2021-03-19 19:57:00
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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 torch
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import gym
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import numpy as np
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import datetime
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from DDPG.agent import DDPG
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from DDPG.env import NormalizedActions,OUNoise
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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 DDPGConfig:
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def __init__(self):
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self.gamma = 0.99
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self.critic_lr = 1e-3
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self.actor_lr = 1e-4
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self.memory_capacity = 10000
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self.batch_size = 128
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self.train_eps =300
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self.train_steps = 200
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self.eval_eps = 200
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self.eval_steps = 200
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self.target_update = 4
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self.hidden_dim = 30
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self.soft_tau=1e-2
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def train(cfg,env,agent):
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print('Start to train ! ')
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ou_noise = OUNoise(env.action_space) # action noise
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rewards = []
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ma_rewards = [] # moving average rewards
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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()
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ou_noise.reset()
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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)
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action = ou_noise.get_action(
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action, i_step) # 即paper中的random process
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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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agent.update()
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state = next_state
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if done:
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break
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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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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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if __name__ == "__main__":
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cfg = DDPGConfig()
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env = NormalizedActions(gym.make("Pendulum-v0"))
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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.shape[0]
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agent = DDPG(n_states,n_actions,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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