137 lines
4.5 KiB
Python
137 lines
4.5 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: 2020-11-22 23:21:53
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LastEditor: John
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LastEditTime: 2021-05-05 17:35:20
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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 torch
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import datetime
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from itertools import count
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from PolicyGradient.agent import PolicyGradient
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from common.plot import plot_rewards
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from common.utils import save_results,make_dir
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curr_time = datetime.datetime.now().strftime(
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"%Y%m%d-%H%M%S") # obtain current time
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class PGConfig:
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def __init__(self):
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self.algo = "PolicyGradient" # name of algo
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self.env = 'CartPole-v0'
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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 = 300 # 训练的episode数目
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self.eval_eps = 50
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self.batch_size = 8
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self.lr = 0.01 # learning rate
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self.gamma = 0.99
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self.hidden_dim = 36 # dimmension of hidden layer
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self.device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu") # check gpu
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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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state_dim = env.observation_space.shape[0]
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agent = PolicyGradient(state_dim,cfg)
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return env,agent
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def train(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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state_pool = [] # 存放每batch_size个episode的state序列
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action_pool = []
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reward_pool = []
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rewards = []
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ma_rewards = []
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for i_episode in range(cfg.train_eps):
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state = env.reset()
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ep_reward = 0
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for _ in count():
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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)
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ep_reward += reward
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if done:
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reward = 0
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state_pool.append(state)
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action_pool.append(float(action))
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reward_pool.append(reward)
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state = next_state
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if done:
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print('Episode:', i_episode, ' Reward:', ep_reward)
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break
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if i_episode > 0 and i_episode % cfg.batch_size == 0:
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agent.update(reward_pool,state_pool,action_pool)
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state_pool = [] # 每个episode的state
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action_pool = []
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reward_pool = []
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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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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 = []
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for i_episode in range(cfg.eval_eps):
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state = env.reset()
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ep_reward = 0
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for _ in count():
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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)
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ep_reward += reward
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if done:
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reward = 0
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state = next_state
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if done:
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print('Episode:', i_episode, ' Reward:', ep_reward)
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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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print('complete evaling！')
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return rewards, ma_rewards
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if __name__ == "__main__":
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cfg = PGConfig()
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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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