144 lines
5.3 KiB
Python
144 lines
5.3 KiB
Python
#!/usr/bin/env python
|
||
# coding=utf-8
|
||
'''
|
||
Author: John
|
||
Email: johnjim0816@gmail.com
|
||
Date: 2020-11-22 23:21:53
|
||
LastEditor: John
|
||
LastEditTime: 2022-07-21 21:44:00
|
||
Discription:
|
||
Environment:
|
||
'''
|
||
import sys,os
|
||
curr_path = os.path.dirname(os.path.abspath(__file__)) # current path
|
||
parent_path = os.path.dirname(curr_path) # parent path
|
||
sys.path.append(parent_path) # add to system path
|
||
|
||
import gym
|
||
import torch
|
||
import datetime
|
||
import argparse
|
||
from itertools import count
|
||
|
||
from pg import PolicyGradient
|
||
from common.utils import save_results, make_dir
|
||
from common.utils import plot_rewards
|
||
|
||
|
||
def get_args():
|
||
""" Hyperparameters
|
||
"""
|
||
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # Obtain current time
|
||
parser = argparse.ArgumentParser(description="hyperparameters")
|
||
parser.add_argument('--algo_name',default='PolicyGradient',type=str,help="name of algorithm")
|
||
parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
|
||
parser.add_argument('--train_eps',default=300,type=int,help="episodes of training")
|
||
parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
|
||
parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
|
||
parser.add_argument('--lr',default=0.01,type=float,help="learning rate")
|
||
parser.add_argument('--batch_size',default=8,type=int)
|
||
parser.add_argument('--hidden_dim',default=36,type=int)
|
||
parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda")
|
||
parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
|
||
'/' + curr_time + '/results/' )
|
||
parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
|
||
'/' + curr_time + '/models/' ) # path to save models
|
||
parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
|
||
args = parser.parse_args()
|
||
return args
|
||
|
||
|
||
def env_agent_config(cfg,seed=1):
|
||
env = gym.make(cfg.env_name)
|
||
env.seed(seed)
|
||
n_states = env.observation_space.shape[0]
|
||
agent = PolicyGradient(n_states,cfg)
|
||
return env,agent
|
||
|
||
def train(cfg,env,agent):
|
||
print('Start training!')
|
||
print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
|
||
state_pool = [] # temp states pool per several episodes
|
||
action_pool = []
|
||
reward_pool = []
|
||
rewards = []
|
||
ma_rewards = []
|
||
for i_ep in range(cfg.train_eps):
|
||
state = env.reset()
|
||
ep_reward = 0
|
||
for _ in count():
|
||
action = agent.choose_action(state) # 根据当前环境state选择action
|
||
next_state, reward, done, _ = env.step(action)
|
||
ep_reward += reward
|
||
if done:
|
||
reward = 0
|
||
state_pool.append(state)
|
||
action_pool.append(float(action))
|
||
reward_pool.append(reward)
|
||
state = next_state
|
||
if done:
|
||
print(f'Episode：{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
|
||
break
|
||
if i_ep > 0 and i_ep % cfg.batch_size == 0:
|
||
agent.update(reward_pool,state_pool,action_pool)
|
||
state_pool = []
|
||
action_pool = []
|
||
reward_pool = []
|
||
rewards.append(ep_reward)
|
||
if ma_rewards:
|
||
ma_rewards.append(
|
||
0.9*ma_rewards[-1]+0.1*ep_reward)
|
||
else:
|
||
ma_rewards.append(ep_reward)
|
||
print('Finish training!')
|
||
env.close() # close environment
|
||
return rewards, ma_rewards
|
||
|
||
|
||
def test(cfg,env,agent):
|
||
print('开始测试!')
|
||
print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
|
||
rewards = []
|
||
ma_rewards = []
|
||
for i_ep in range(cfg.test_eps):
|
||
state = env.reset()
|
||
ep_reward = 0
|
||
for _ in count():
|
||
action = agent.choose_action(state) # 根据当前环境state选择action
|
||
next_state, reward, done, _ = env.step(action)
|
||
ep_reward += reward
|
||
if done:
|
||
reward = 0
|
||
state = next_state
|
||
if done:
|
||
print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
|
||
break
|
||
rewards.append(ep_reward)
|
||
if ma_rewards:
|
||
ma_rewards.append(
|
||
0.9*ma_rewards[-1]+0.1*ep_reward)
|
||
else:
|
||
ma_rewards.append(ep_reward)
|
||
print('完成测试！')
|
||
env.close()
|
||
return rewards, ma_rewards
|
||
|
||
if __name__ == "__main__":
|
||
cfg = Config()
|
||
# 训练
|
||
env, agent = env_agent_config(cfg)
|
||
rewards, ma_rewards = train(cfg, env, agent)
|
||
make_dir(cfg.result_path, cfg.model_path) # 创建保存结果和模型路径的文件夹
|
||
agent.save(path=cfg.model_path) # 保存模型
|
||
save_results(rewards, ma_rewards, tag='train',
|
||
path=cfg.result_path) # 保存结果
|
||
plot_rewards(rewards, ma_rewards, cfg, tag="train") # 画出结果
|
||
# 测试
|
||
env, agent = env_agent_config(cfg)
|
||
agent.load(path=cfg.model_path) # 导入模型
|
||
rewards, ma_rewards = test(cfg, env, agent)
|
||
save_results(rewards, ma_rewards, tag='test',
|
||
path=cfg.result_path) # 保存结果
|
||
plot_rewards(rewards, ma_rewards, cfg, tag="test") # 画出结果
|
||
|