Move learning rate reporting

ml-agents/mlagents/trainers/agent_processor.py

         if take_action_outputs:
             for _entropy in take_action_outputs["entropy"]:
                 self.stats_reporter.add_stat("Policy/Entropy", _entropy)
-            self.stats_reporter.add_stat(
-                "Policy/Learning Rate", take_action_outputs["learning_rate"]
-            )
 
         terminated_agents: Set[str] = set()
         # Make unique agent_ids that are global across workers

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

                 self.stats_name_to_update_name = {
                     "Losses/Value Loss": "value_loss",
                     "Losses/Policy Loss": "policy_loss",
+                    "Policy/Learning Rate": "learning_rate",
                 }
                 if self.policy.use_recurrent:
                     self.m_size = self.policy.m_size
                     "value_loss": self.value_loss,
                     "policy_loss": self.abs_policy_loss,
                     "update_batch": self.update_batch,
+                    "learning_rate": self.learning_rate,
-            # Add some stuff to inference dict from optimizer
-            self.policy.inference_dict["learning_rate"] = self.learning_rate
             self.policy.initialize_or_load()
 
     def create_cc_critic(

ml-agents/mlagents/trainers/sac/optimizer.py

             "Losses/Q1 Loss": "q1_loss",
             "Losses/Q2 Loss": "q2_loss",
             "Policy/Entropy Coeff": "entropy_coef",
+            "Policy/Learning Rate": "learning_rate",
         }
 
         self.update_dict = {
             "update_batch": self.update_batch_policy,
             "update_value": self.update_batch_value,
             "update_entropy": self.update_batch_entropy,
+            "learning_rate": self.learning_rate,
-
-        # Add some stuff to inference dict from optimizer
-        self.policy.inference_dict["learning_rate"] = self.learning_rate
 
     def create_inputs_and_outputs(self) -> None:
         """