update projects

projects/codes/QLearning/task0.py

@@ -0,0 +1,141 @@
+#!/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
+        
+