137 lines
5.7 KiB
Python
137 lines
5.7 KiB
Python
#!/usr/bin/env python
|
|
# coding=utf-8
|
|
'''
|
|
Author: JiangJi
|
|
Email: johnjim0816@gmail.com
|
|
Date: 2021-11-07 18:10:37
|
|
LastEditor: JiangJi
|
|
LastEditTime: 2022-07-21 00:08:38
|
|
Discription:
|
|
'''
|
|
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 common.utils import save_results,make_dir
|
|
from common.utils import plot_rewards,save_args
|
|
from DoubleDQN.double_dqn import DoubleDQN
|
|
|
|
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='DoubleDQN',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=200,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('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
|
|
parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
|
|
parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon")
|
|
parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
|
|
parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity")
|
|
parser.add_argument('--batch_size',default=64,type=int)
|
|
parser.add_argument('--target_update',default=2,type=int)
|
|
parser.add_argument('--hidden_dim',default=256,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]
|
|
n_actions = env.action_space.n
|
|
agent = DoubleDQN(n_states,n_actions,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}')
|
|
rewards = [] # 记录所有回合的奖励
|
|
ma_rewards = [] # 记录所有回合的滑动平均奖励
|
|
for i_ep in range(cfg.train_eps):
|
|
ep_reward = 0 # 记录一回合内的奖励
|
|
state = env.reset() # 重置环境，返回初始状态
|
|
while True:
|
|
action = agent.choose_action(state)
|
|
next_state, reward, done, _ = env.step(action)
|
|
ep_reward += reward
|
|
agent.memory.push(state, action, reward, next_state, done)
|
|
state = next_state
|
|
agent.update()
|
|
if done:
|
|
break
|
|
if i_ep % cfg.target_update == 0:
|
|
agent.target_net.load_state_dict(agent.policy_net.state_dict())
|
|
if (i_ep+1)%10 == 0:
|
|
print(f'Env:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
|
|
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!')
|
|
return rewards,ma_rewards
|
|
|
|
def test(cfg,env,agent):
|
|
print('Start testing')
|
|
print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
|
|
############# 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0 ###############
|
|
cfg.epsilon_start = 0.0 # e-greedy策略中初始epsilon
|
|
cfg.epsilon_end = 0.0 # e-greedy策略中的终止epsilon
|
|
################################################################################
|
|
rewards = [] # 记录所有回合的奖励
|
|
ma_rewards = [] # 记录所有回合的滑动平均奖励
|
|
|
|
for i_ep in range(cfg.test_eps):
|
|
state = env.reset()
|
|
ep_reward = 0
|
|
while True:
|
|
action = agent.choose_action(state)
|
|
next_state, reward, done, _ = env.step(action)
|
|
state = next_state
|
|
ep_reward += reward
|
|
if done:
|
|
break
|
|
rewards.append(ep_reward)
|
|
if ma_rewards:
|
|
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
|
else:
|
|
ma_rewards.append(ep_reward)
|
|
print(f"Epside:{i_ep+1}/{cfg.test_eps}, Reward:{ep_reward:.1f}")
|
|
print('Finish testing!')
|
|
return rewards,ma_rewards
|
|
|
|
if __name__ == "__main__":
|
|
cfg = get_args()
|
|
print(cfg.device)
|
|
# training
|
|
env,agent = env_agent_config(cfg,seed=1)
|
|
rewards, ma_rewards = train(cfg, env, agent)
|
|
make_dir(cfg.result_path, cfg.model_path)
|
|
save_args(cfg)
|
|
agent.save(path=cfg.model_path)
|
|
save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
|
|
plot_rewards(rewards, ma_rewards, cfg, tag="train")
|
|
# testing
|
|
env,agent = env_agent_config(cfg,seed=10)
|
|
agent.load(path=cfg.model_path)
|
|
rewards,ma_rewards = test(cfg,env,agent)
|
|
save_results(rewards,ma_rewards,tag = 'test',path = cfg.result_path)
|
|
plot_rewards(rewards, ma_rewards, cfg, tag="test")
|