Fix batch size issue with BC (#2965) (#2966)

ml-agents/mlagents/trainers/bc/trainer.py

         """
         self.demonstration_buffer.update_buffer.shuffle(self.policy.sequence_length)
         batch_losses = []
+        batch_size = self.n_sequences * self.policy.sequence_length
+        # We either divide the entire buffer into num_batches batches, or limit the number
+        # of batches to batches_per_epoch.
-            len(self.demonstration_buffer.update_buffer["actions"]) // self.n_sequences,
+            len(self.demonstration_buffer.update_buffer["actions"]) // batch_size,
-
-        batch_size = self.n_sequences * self.policy.sequence_length
 
         for i in range(0, num_batches * batch_size, batch_size):
             update_buffer = self.demonstration_buffer.update_buffer

ml-agents/mlagents/trainers/tests/test_bc.py

             use_recurrent: false
             sequence_length: 32
             memory_size: 32
-            batches_per_epoch: 1
+            batches_per_epoch: 100 # Force code to use all possible batches
             batch_size: 32
             summary_freq: 2000
             max_steps: 4000
 
-def create_bc_trainer(dummy_config, is_discrete=False):
+def create_bc_trainer(dummy_config, is_discrete=False, use_recurrent=False):
     mock_env = mock.Mock()
     if is_discrete:
         mock_brain = mb.create_mock_pushblock_brain()
     trainer_parameters["demo_path"] = (
         os.path.dirname(os.path.abspath(__file__)) + "/test.demo"
     )
+    trainer_parameters["use_recurrent"] = use_recurrent
     trainer = BCTrainer(
         mock_brain, trainer_parameters, training=True, load=False, seed=0, run_id=0
     )
 
-def test_bc_trainer_step(dummy_config):
-    trainer, env = create_bc_trainer(dummy_config)
+@pytest.mark.parametrize("use_recurrent", [True, False])
+def test_bc_trainer_step(dummy_config, use_recurrent):
+    trainer, env = create_bc_trainer(dummy_config, use_recurrent=use_recurrent)
     # Test get_step
     assert trainer.get_step == 0
     # Test update policy