142 lines
5.8 KiB
Python
142 lines
5.8 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: 2022-06-21 19:36:05
|
||
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
|
||
|
||
from env.gridworld_env import CliffWalkingWapper
|
||
from qlearning import QLearning
|
||
from common.utils import plot_rewards
|
||
from common.utils import save_results,make_dir
|
||
|
||
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
|
||
class Config:
|
||
'''超参数
|
||
'''
|
||
|
||
def __init__(self):
|
||
################################## 环境超参数 ###################################
|
||
self.algo_name = 'Q-learning' # 算法名称
|
||
self.env_name = 'CliffWalking-v0' # 环境名称
|
||
self.device = torch.device(
|
||
"cuda" if torch.cuda.is_available() else "cpu") # 检测GPUgjgjlkhfsf风刀霜的撒发十
|
||
self.seed = 10 # 随机种子，置0则不设置随机种子
|
||
self.train_eps = 400 # 训练的回合数
|
||
self.test_eps = 30 # 测试的回合数
|
||
################################################################################
|
||
|
||
################################## 算法超参数 ###################################
|
||
self.gamma = 0.90 # 强化学习中的折扣因子
|
||
self.epsilon_start = 0.95 # e-greedy策略中初始epsilon
|
||
self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
|
||
self.epsilon_decay = 300 # e-greedy策略中epsilon的衰减率
|
||
self.lr = 0.1 # 学习率
|
||
################################################################################
|
||
|
||
################################# 保存结果相关参数 ################################
|
||
self.result_path = curr_path + "/outputs/" + self.env_name + \
|
||
'/' + curr_time + '/results/' # 保存结果的路径
|
||
self.model_path = curr_path + "/outputs/" + self.env_name + \
|
||
'/' + curr_time + '/models/' # 保存模型的路径
|
||
self.save = True # 是否保存图片
|
||
################################################################################
|
||
|
||
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
|
||
ma_rewards = [] # 记录滑动平均奖励
|
||
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.choose_action(state) # 根据算法选择一个动作
|
||
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)
|
||
if ma_rewards:
|
||
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
||
else:
|
||
ma_rewards.append(ep_reward)
|
||
print("回合数：{}/{}，奖励{:.1f}".format(i_ep+1, cfg.train_eps,ep_reward))
|
||
print('完成训练！')
|
||
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 = [] # 记录所有回合的奖励
|
||
ma_rewards = [] # 滑动平均的奖励
|
||
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)
|
||
if ma_rewards:
|
||
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
||
else:
|
||
ma_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,ma_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
|
||
agent = QLearning(n_states,n_actions,cfg)
|
||
return env,agent
|
||
if __name__ == "__main__":
|
||
cfg = Config()
|
||
# 训练
|
||
env, agent = env_agent_config(cfg, seed=1)
|
||
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, seed=10)
|
||
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
|
||
|
||
|