139 lines
5.8 KiB
Python
139 lines
5.8 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: JiangJi
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Email: johnjim0816@gmail.com
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Date: 2021-11-07 18:10:37
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LastEditor: JiangJi
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LastEditTime: 2022-07-21 21:52:31
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Discription:
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'''
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import sys,os
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curr_path = os.path.dirname(os.path.abspath(__file__)) # current path
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parent_path = os.path.dirname(curr_path) # parent path
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sys.path.append(parent_path) # add to system path
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import gym
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import torch
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import datetime
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import argparse
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from common.utils import save_results,make_dir
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from common.utils import plot_rewards,save_args
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from DoubleDQN.double_dqn import DoubleDQN
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def get_args():
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""" Hyperparameters
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"""
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # Obtain current time
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parser = argparse.ArgumentParser(description="hyperparameters")
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parser.add_argument('--algo_name',default='DoubleDQN',type=str,help="name of algorithm")
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parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
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parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
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parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
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parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
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parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
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parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
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parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon")
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parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
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parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity")
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parser.add_argument('--batch_size',default=64,type=int)
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parser.add_argument('--target_update',default=2,type=int)
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parser.add_argument('--hidden_dim',default=256,type=int)
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parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda")
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parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/results/' )
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parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/models/' ) # path to save models
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parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
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args = parser.parse_args()
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return args
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def env_agent_config(cfg,seed=1):
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env = gym.make(cfg.env_name)
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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 = DoubleDQN(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 training!')
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print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
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rewards = [] # 记录所有回合的奖励
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ma_rewards = [] # 记录所有回合的滑动平均奖励
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for i_ep in range(cfg.train_eps):
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ep_reward = 0 # 记录一回合内的奖励
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state = env.reset() # 重置环境，返回初始状态
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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 % 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(f'Env:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
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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('Finish training!')
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return {'rewards':rewards,'ma_rewards':ma_rewards}
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def test(cfg,env,agent):
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print('Start testing')
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print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
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############# 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0 ###############
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cfg.epsilon_start = 0.0 # e-greedy策略中初始epsilon
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cfg.epsilon_end = 0.0 # e-greedy策略中的终止epsilon
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################################################################################
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rewards = [] # 记录所有回合的奖励
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ma_rewards = [] # 记录所有回合的滑动平均奖励
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for i_ep in range(cfg.test_eps):
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state = env.reset()
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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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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"Epside:{i_ep+1}/{cfg.test_eps}, Reward:{ep_reward:.1f}")
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print('Finish testing!')
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return {'rewards':rewards,'ma_rewards':ma_rewards}
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if __name__ == "__main__":
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cfg = get_args()
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print(cfg.device)
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# training
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env,agent = env_agent_config(cfg,seed=1)
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res_dic = train(cfg, env, agent)
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make_dir(cfg.result_path, cfg.model_path)
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save_args(cfg)
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agent.save(path=cfg.model_path)
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save_results(res_dic, tag='train',
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path=cfg.result_path)
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plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train")
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# testing
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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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res_dic = test(cfg,env,agent)
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save_results(res_dic, tag='test',
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path=cfg.result_path)
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plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="test")
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