update

codes/DQN/task0.py

@@ -25,6 +25,7 @@ class Config:
         self.env_name = 'CartPole-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 = 200  # 训练的回合数
         self.test_eps = 30  # 测试的回合数
         ################################################################################
@@ -41,7 +42,7 @@ class Config:
         self.hidden_dim = 256  # 网络隐藏层
         ################################################################################
 
-        ################################# 保存结果相关参数 ################################
+        ################################# 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 + \
@@ -50,17 +51,17 @@ class Config:
         ################################################################################
 
 
-def env_agent_config(cfg, seed=1):
+def env_agent_config(cfg):
     ''' 创建环境和智能体
     '''
     env = gym.make(cfg.env_name)  # 创建环境
     state_dim = env.observation_space.shape[0]  # 状态维度
     action_dim = env.action_space.n  # 动作维度
     agent = DQN(state_dim, action_dim, cfg)  # 创建智能体
-    if seed !=0: # 设置随机种子
-        torch.manual_seed(seed)
-        env.seed(seed)
-        np.random.seed(seed)
+    if cfg.seed !=0: # 设置随机种子
+        torch.manual_seed(cfg.seed)
+        env.seed(cfg.seed)
+        np.random.seed(cfg.seed)
     return env, agent
 
 
@@ -94,15 +95,17 @@ def train(cfg, env, agent):
         if (i_ep + 1) % 10 == 0:
             print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
     print('完成训练！')
+    env.close()
     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}')
-    # 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0
+    ############# 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):
@@ -122,13 +125,14 @@ def test(cfg, env, agent):
             ma_rewards.append(ep_reward)
         print(f"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
     print('完成测试！')
+    env.close()
     return rewards, ma_rewards
 
 
 if __name__ == "__main__":
     cfg = Config()
     # 训练
-    env, agent = env_agent_config(cfg, seed=1)
+    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
@@ -136,7 +140,7 @@ if __name__ == "__main__":
                  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)
+    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',

codes/DoubleDQN/task0.py

@@ -5,7 +5,7 @@ Author: JiangJi
 Email: johnjim0816@gmail.com
 Date: 2021-11-07 18:10:37
 LastEditor: JiangJi
-LastEditTime: 2021-11-19 18:34:05
+LastEditTime: 2021-12-29 15:02:30
 Discription: 
 '''
 
@@ -20,20 +20,22 @@ import datetime
 
 from common.utils import save_results, make_dir
 from common.utils import plot_rewards
-from DoubleDQN.agent import DoubleDQN
-from DoubleDQN.train import train,test
+from DoubleDQN.double_dqn import DoubleDQN
 
 curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
-algo_name = 'DoubleDQN'  # 算法名称
-env_name = 'CartPole-v0'  # 环境名称
-class DoubleDQNConfig:
+
+class Config:
     def __init__(self):
-        self.algo_name = algo_name # 算法名称
-        self.env_name = env_name  # 环境名称
+        ################################## 环境超参数 ###################################
+        self.algo_name = 'DoubleDQN' # 算法名称
+        self.env_name = 'CartPole-v0'  # 环境名称
         self.device = torch.device(
             "cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
         self.train_eps = 200  # 训练的回合数
         self.test_eps = 30  # 测试的回合数
+        ################################################################################
+
+        ################################## 算法超参数 ###################################
         self.gamma = 0.95  # 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
@@ -43,20 +45,16 @@ class DoubleDQNConfig:
         self.batch_size = 64  # mini-batch SGD中的批量大小
         self.target_update = 2  # 目标网络的更新频率
         self.hidden_dim = 256  # 网络隐藏层
-class PlotConfig:
-    ''' 绘图相关参数设置
-    '''
+        ################################################################################
 
-    def __init__(self) -> None:
-        self.algo_name = algo_name  # 算法名称
-        self.env_name = env_name  # 环境名称
-        self.device = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
+        ################################# 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
+        self.save = True # 是否保存图片
+        ################################################################################
+        
         
 def env_agent_config(cfg,seed=1):
     env = gym.make(cfg.env_name)  
@@ -66,18 +64,81 @@ def env_agent_config(cfg,seed=1):
     agent = DoubleDQN(state_dim,action_dim,cfg)
     return env,agent
 
-cfg = DoubleDQNConfig()
-plot_cfg = PlotConfig()
-# 训练
-env,agent = env_agent_config(cfg,seed=1)
-rewards, ma_rewards = train(cfg, env, agent)
-make_dir(plot_cfg.result_path, plot_cfg.model_path)  # 创建保存结果和模型路径的文件夹
-agent.save(path=plot_cfg.model_path)  # 保存模型
-save_results(rewards, ma_rewards, tag='train', path=plot_cfg.result_path)  # 保存结果
-plot_rewards(rewards, ma_rewards, plot_cfg, tag="train")  # 画出结果
-# 测试
-env,agent = env_agent_config(cfg,seed=10)
-agent.load(path=plot_cfg.model_path)  # 导入模型
-rewards,ma_rewards = test(cfg,env,agent)
-save_results(rewards, ma_rewards, tag='test', path=plot_cfg.result_path)  # 保存结果
-plot_rewards(rewards, ma_rewards, plot_cfg, tag="test")  # 画出结果
+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'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward}')
+        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('完成训练！')
+    env.close()
+    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}')
+    ############# 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"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
+    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

codes/DoubleDQN/train.py

@@ -1,73 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: JiangJi
-Email: johnjim0816@gmail.com
-Date: 2021-11-07 18:10:37
-LastEditor: JiangJi
-LastEditTime: 2021-11-19 18:34:05
-Discription: 
-'''
-
-import sys,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
-    
-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'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward}')
-        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}')
-    # 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"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
-    print('完成测试！')
-    return rewards,ma_rewards    
-    

codes/Logs.md

@@ -1,5 +1,7 @@
 ## 记录笔者更新的日志
 
+**2021.12.28-1**：将```task.py```中的两个Config类合并为一个，并加以注释便于阅读，从DQN算法开始更新
+
 **2021.12.22-3**：将```agent.py```更改为对应的算法名称，便于区分如```dqn```与```dqn_cnn```的情况  
 **2021.12.22-2**：简化了代码结构，将原来的```train.py```和```task.py```等合并到```task.py```中  
 **2021.12.22-1**：简化了代码结构，将原来的```model.py```和```memory.py```等合并到```agent.py```中，```plot.py```的内容合并到```common.utils.py```中

codes/QLearning/train.py

@@ -19,7 +19,6 @@ def train(cfg,env,agent):
             ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
         else:
             ma_rewards.append(ep_reward)
-        if ()
         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