97 lines
3.6 KiB
Python
97 lines
3.6 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: 2021-03-22 16:18:10
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LastEditor: John
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LastEditTime: 2021-04-11 01:25:43
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Discription:
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Environment:
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'''
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import sys,os
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curr_path = os.path.dirname(__file__)
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parent_path=os.path.dirname(curr_path)
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sys.path.append(parent_path) # add current terminal path to sys.path
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import gym
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import numpy as np
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import torch
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import datetime
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from PPO.agent import PPO
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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 PPOConfig:
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def __init__(self) -> None:
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self.env = 'LunarLander-v2'
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self.algo = 'PPO'
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self.batch_size = 128
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self.gamma=0.95
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self.n_epochs = 4
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self.actor_lr = 0.002
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self.critic_lr = 0.005
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self.gae_lambda=0.95
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self.policy_clip=0.2
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self.hidden_dim = 256
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self.update_fre = 20 # frequency of agent update
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self.train_eps = 300 # max training episodes
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self.train_steps = 1000
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check gpu
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def train(cfg,env,agent):
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best_reward = env.reward_range[0]
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rewards= []
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ma_rewards = [] # moving average rewards
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avg_reward = 0
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running_steps = 0
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for i_episode 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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# for i_step in range(cfg.train_steps):
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while not done:
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action, prob, val = agent.choose_action(state)
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state_, reward, done, _ = env.step(action)
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running_steps += 1
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ep_reward += reward
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agent.memory.push(state, action, prob, val, reward, done)
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if running_steps % cfg.update_fre == 0:
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agent.update()
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state = state_
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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(
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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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avg_reward = np.mean(rewards[-100:])
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if avg_reward > best_reward:
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best_reward = avg_reward
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agent.save(path=SAVED_MODEL_PATH)
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print('Episode:{}/{}, Reward:{:.1f}, avg reward:{:.1f}, Loss:{}'.format(i_episode+1,cfg.train_eps,ep_reward,avg_reward,agent.loss))
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return rewards,ma_rewards
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if __name__ == '__main__':
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cfg = PPOConfig()
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env = gym.make(cfg.env)
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env.seed(1)
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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 = PPO(state_dim,action_dim,cfg)
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rewards,ma_rewards = train(cfg,env,agent)
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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) |