update

codes/DDPG/agent.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-09 20:25:52
 @LastEditor: John
-LastEditTime: 2021-03-31 00:56:32
+LastEditTime: 2021-05-04 14:50:17
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -26,6 +26,7 @@ class DDPG:
         self.target_critic = Critic(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
         self.target_actor = Actor(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
 
+        # copy parameters to target net
         for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
             target_param.data.copy_(param.data)
         for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
@@ -42,7 +43,6 @@ class DDPG:
     def choose_action(self, state):
         state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
         action = self.actor(state)
-        # torch.detach()用于切断反向传播
         return action.detach().cpu().numpy()[0, 0]
 
     def update(self):
@@ -50,13 +50,13 @@ class DDPG:
             return
         state, action, reward, next_state, done = self.memory.sample(
             self.batch_size)
-        # 将所有变量转为张量
+        # convert variables to Tensor
         state = torch.FloatTensor(state).to(self.device)
         next_state = torch.FloatTensor(next_state).to(self.device)
         action = torch.FloatTensor(action).to(self.device)
         reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
         done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device)
-        # 注意critic将(s_t,a)作为输入
+       
         policy_loss = self.critic(state, self.actor(state))
         policy_loss = -policy_loss.mean()
         next_action = self.target_actor(next_state)