Removing misleading learning rate

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

             self.trainer_settings.max_steps,
         )
 
-        self.optimizer = torch.optim.Adam(
-            params, lr=self.trainer_settings.hyperparameters.learning_rate
-        )
+        self.optimizer = torch.optim.Adam(params)
         self.stats_name_to_update_name = {
             "Losses/Value Loss": "value_loss",
             "Losses/Policy Loss": "policy_loss",

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

             1e-10,
             self.trainer_settings.max_steps,
         )
-        self.policy_optimizer = torch.optim.Adam(
-            policy_params, lr=hyperparameters.learning_rate
-        )
-        self.value_optimizer = torch.optim.Adam(
-            value_params, lr=hyperparameters.learning_rate
-        )
-        self.entropy_optimizer = torch.optim.Adam(
-            self._log_ent_coef.parameters(), lr=hyperparameters.learning_rate
-        )
+        self.policy_optimizer = torch.optim.Adam(policy_params)
+        self.value_optimizer = torch.optim.Adam(value_params)
+        self.entropy_optimizer = torch.optim.Adam(self._log_ent_coef.parameters())
         self._move_to_device(default_device())
 
     @property

ml-agents/mlagents/trainers/torch/components/bc/module.py

             learning_rate_schedule, self.current_lr, 1e-10, self._anneal_steps
         )
         params = self.policy.actor.parameters()
-        self.optimizer = torch.optim.Adam(params, lr=self.current_lr)
+        self.optimizer = torch.optim.Adam(params)
         _, self.demonstration_buffer = demo_to_buffer(
             settings.demo_path, policy.sequence_length, policy.behavior_spec
         )