128 lines
5.5 KiB
Python
128 lines
5.5 KiB
Python
#!/usr/bin/env python
|
||
# coding=utf-8
|
||
'''
|
||
Author: John
|
||
Email: johnjim0816@gmail.com
|
||
Date: 2020-09-11 23:03:00
|
||
LastEditor: John
|
||
LastEditTime: 2022-08-10 11:25:56
|
||
Discription:
|
||
Environment:
|
||
'''
|
||
import sys
|
||
import os
|
||
curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
|
||
parent_path = os.path.dirname(curr_path) # 父路径
|
||
sys.path.append(parent_path) # 添加路径到系统路径
|
||
|
||
import gym
|
||
import torch
|
||
import datetime
|
||
import argparse
|
||
from envs.gridworld_env import CliffWalkingWapper
|
||
from qlearning import QLearning
|
||
from common.utils import plot_rewards,save_args
|
||
from common.utils import save_results,make_dir
|
||
|
||
def get_args():
|
||
"""
|
||
"""
|
||
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
|
||
parser = argparse.ArgumentParser(description="hyperparameters")
|
||
parser.add_argument('--algo_name',default='Q-learning',type=str,help="name of algorithm")
|
||
parser.add_argument('--env_name',default='CliffWalking-v0',type=str,help="name of environment")
|
||
parser.add_argument('--train_eps',default=400,type=int,help="episodes of training") # 训练的回合数
|
||
parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") # 测试的回合数
|
||
parser.add_argument('--gamma',default=0.90,type=float,help="discounted factor") # 折扣因子
|
||
parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon") # e-greedy策略中初始epsilon
|
||
parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") # e-greedy策略中的终止epsilon
|
||
parser.add_argument('--epsilon_decay',default=300,type=int,help="decay rate of epsilon") # e-greedy策略中epsilon的衰减率
|
||
parser.add_argument('--lr',default=0.1,type=float,help="learning rate")
|
||
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/',type=str )
|
||
parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
|
||
'/' + curr_time + '/models/',type=str,help="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
|
||
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
|
||
|
||
def train(cfg,env,agent):
|
||
print('开始训练！')
|
||
print(f'环境:{cfg.env_name}, 算法:{cfg.algo_name}, 设备:{cfg.device}')
|
||
rewards = [] # 记录奖励
|
||
for i_ep in range(cfg.train_eps):
|
||
ep_reward = 0 # 记录每个回合的奖励
|
||
state = env.reset() # 重置环境,即开始新的回合
|
||
while True:
|
||
action = agent.sample(state) # 根据算法采样一个动作
|
||
next_state, reward, done, _ = env.step(action) # 与环境进行一次动作交互
|
||
agent.update(state, action, reward, next_state, done) # Q学习算法更新
|
||
state = next_state # 更新状态
|
||
ep_reward += reward
|
||
if done:
|
||
break
|
||
rewards.append(ep_reward)
|
||
print(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.1f}，Epsilon：{agent.epsilon}")
|
||
print('完成训练！')
|
||
return {"rewards":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 = [] # 记录所有回合的奖励
|
||
for i_ep in range(cfg.test_eps):
|
||
ep_reward = 0 # 记录每个episode的reward
|
||
state = env.reset() # 重置环境, 重新开一局（即开始新的一个回合）
|
||
while True:
|
||
action = agent.predict(state) # 根据算法选择一个动作
|
||
next_state, reward, done, _ = env.step(action) # 与环境进行一个交互
|
||
state = next_state # 更新状态
|
||
ep_reward += reward
|
||
if done:
|
||
break
|
||
rewards.append(ep_reward)
|
||
print(f"回合数：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}")
|
||
print('完成测试！')
|
||
return {"rewards":rewards}
|
||
|
||
def env_agent_config(cfg,seed=1):
|
||
'''创建环境和智能体
|
||
Args:
|
||
cfg ([type]): [description]
|
||
seed (int, optional): 随机种子. Defaults to 1.
|
||
Returns:
|
||
env [type]: 环境
|
||
agent : 智能体
|
||
'''
|
||
env = gym.make(cfg.env_name)
|
||
env = CliffWalkingWapper(env)
|
||
env.seed(seed) # 设置随机种子
|
||
n_states = env.observation_space.n # 状态维度
|
||
n_actions = env.action_space.n # 动作维度
|
||
print(f"状态数：{n_states}，动作数：{n_actions}")
|
||
agent = QLearning(n_actions,cfg)
|
||
return env,agent
|
||
if __name__ == "__main__":
|
||
cfg = get_args()
|
||
# 训练
|
||
env, agent = env_agent_config(cfg)
|
||
res_dic = train(cfg, env, agent)
|
||
make_dir(cfg.result_path, cfg.model_path)
|
||
save_args(cfg) # save parameters
|
||
agent.save(path=cfg.model_path) # save model
|
||
save_results(res_dic, tag='train',
|
||
path=cfg.result_path)
|
||
plot_rewards(res_dic['rewards'], cfg, tag="train")
|
||
# 测试
|
||
env, agent = env_agent_config(cfg)
|
||
agent.load(path=cfg.model_path) # 导入模型
|
||
res_dic = test(cfg, env, agent)
|
||
save_results(res_dic, tag='test',
|
||
path=cfg.result_path) # 保存结果
|
||
plot_rewards(res_dic['rewards'], cfg, tag="test") # 画出结果
|
||
|
||
|
||
|