revert bc default batch/epoch

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

                 self.policy,
                 trainer_settings.behavioral_cloning,
                 policy_learning_rate=trainer_settings.hyperparameters.learning_rate,
+                default_batch_size=trainer_settings.hyperparameters.batch_size,
+                default_num_epoch=3,
             )
 
     def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:

ml-agents/mlagents/trainers/settings.py

     steps: int = 0
     strength: float = 1.0
     samples_per_update: int = 0
-    num_epoch: int = 3
-    batch_size: int = 1024
+    # Setting either of these to None will allow the Optimizer
+    # to decide these parameters, based on Trainer hyperparams
+    num_epoch: Optional[int] = None
+    batch_size: Optional[int] = None
 
 
 @attr.s(auto_attribs=True)

ml-agents/mlagents/trainers/tests/torch/test_bcmodule.py

         policy,
         settings=bc_settings,
         policy_learning_rate=trainer_config.hyperparameters.learning_rate,
+        default_batch_size=trainer_config.hyperparameters.batch_size,
+        default_num_epoch=3,
     )
     return bc_module
 

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

         policy: TorchPolicy,
         settings: BehavioralCloningSettings,
         policy_learning_rate: float,
+        default_batch_size: int,
+        default_num_epoch: int,
     ):
         """
         A BC trainer that can be used inline with RL.
         )
         params = self.policy.actor_critic.parameters()
         self.optimizer = torch.optim.Adam(params, lr=self.current_lr)
-
-        self.batch_size = settings.batch_size
-        self.num_epoch = settings.num_epoch
+        self.batch_size = (
+            settings.batch_size if settings.batch_size else default_batch_size
+        )
+        self.num_epoch = settings.num_epoch if settings.num_epoch else default_num_epoch
         self.n_sequences = max(
             min(self.batch_size, self.demonstration_buffer.num_experiences)
             // policy.sequence_length,