134 lines
5.3 KiB
Python
134 lines
5.3 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: 2022-07-21 21:51:34
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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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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 datetime
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import gym
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import torch
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import argparse
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from env import NormalizedActions,OUNoise
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from ddpg import DDPG
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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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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='DDPG',type=str,help="name of algorithm")
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parser.add_argument('--env_name',default='Pendulum-v1',type=str,help="name of environment")
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parser.add_argument('--train_eps',default=300,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('--critic_lr',default=1e-3,type=float,help="learning rate of critic")
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parser.add_argument('--actor_lr',default=1e-4,type=float,help="learning rate of actor")
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parser.add_argument('--memory_capacity',default=8000,type=int,help="memory capacity")
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parser.add_argument('--batch_size',default=128,type=int)
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parser.add_argument('--target_update',default=2,type=int)
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parser.add_argument('--soft_tau',default=1e-2,type=float)
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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 = NormalizedActions(gym.make(cfg.env_name)) # 装饰action噪声
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env.seed(seed) # 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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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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ou_noise = OUNoise(env.action_space) # noise of action
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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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state = env.reset()
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ou_noise.reset()
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done = False
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ep_reward = 0
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i_step = 0
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while not done:
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i_step += 1
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action = agent.choose_action(state)
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action = ou_noise.get_action(action, i_step)
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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 (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(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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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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done = False
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ep_reward = 0
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i_step = 0
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while not done:
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i_step += 1
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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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state = next_state
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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(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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# 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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