remove target update

ml-agents/mlagents/trainers/ppo/optimizer_torch.py

 
         self.stream_names = list(self.reward_signals.keys())
 
-        ModelUtils.soft_update(
-            self.policy.actor_critic.critic, self.policy.actor_critic.target, 1.0
-        )
+        # ModelUtils.soft_update(
+        #    self.policy.actor_critic.critic, self.policy.actor_critic.target, 1.0
+        # )
 
     def ppo_value_loss(
         self,
         old_log_probs = ActionLogProbs.from_dict(batch).flatten()
         log_probs = log_probs.flatten()
         loss_masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.bool)
-        #q_loss = self.ppo_value_loss(qs, old_values, returns_q, decay_eps, loss_masks)
+        # q_loss = self.ppo_value_loss(qs, old_values, returns_q, decay_eps, loss_masks)
         baseline_loss = self.ppo_value_loss(
             baseline_vals, old_marg_values, returns_b, decay_eps, loss_masks
         )
 
         self.optimizer.step()
 
-        #ModelUtils.soft_update(
+        # ModelUtils.soft_update(
-        #)
+        # )
-            #"Losses/Q Loss": q_loss.item(),
+            # "Losses/Q Loss": q_loss.item(),
             "Losses/Baseline Value Loss": baseline_loss.item(),
             "Policy/Learning Rate": decay_lr,
             "Policy/Epsilon": decay_eps,