update

codes/DQN/agent.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:50:49
 @LastEditor: John
-LastEditTime: 2021-05-07 16:30:05
+LastEditTime: 2021-09-15 02:18:56
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -37,18 +37,20 @@ class DQN:
         self.batch_size = cfg.batch_size
         self.policy_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
         self.target_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
-        for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # copy params from policy net
+        for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # 复制参数到目标网路targe_net
             target_param.data.copy_(param.data)
-        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
+        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) # 优化器
         self.memory = ReplayBuffer(cfg.memory_capacity)
-        
 
     def choose_action(self, state):
         '''选择动作
         '''
         self.frame_idx += 1
         if random.random() > self.epsilon(self.frame_idx):
-            action = self.predict(state)
+            with torch.no_grad():
+                state = torch.tensor([state], device=self.device, dtype=torch.float32)
+                q_values = self.policy_net(state)
+                action = q_values.max(1)[1].item()
         else:
             action = random.randrange(self.action_dim)
         return action