Bugfix for LSTM+BC (#2679)

* Fix LSTM+BC in discrete case
* Add test for Barracuda export
* Fix LSTM training for BC

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

             for size in self.act_size:
                 policy_branches.append(
                     tf.layers.dense(
-                        hidden,
+                        hidden_reg,
                         size,
                         activation=None,
                         use_bias=False,

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

             len(self.demonstration_buffer.update_buffer["actions"]) // self.n_sequences,
             self.batches_per_epoch,
         )
-        for i in range(num_batches):
+
+        batch_size = self.n_sequences * self.policy.sequence_length
+
+        for i in range(0, num_batches * batch_size, batch_size):
-            start = i * self.n_sequences
-            end = (i + 1) * self.n_sequences
-            mini_batch = update_buffer.make_mini_batch(start, end)
+            mini_batch = update_buffer.make_mini_batch(i, i + batch_size)
             run_out = self.policy.update(mini_batch, self.n_sequences)
             loss = run_out["policy_loss"]
             batch_losses.append(loss)

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

     return mock_brain
 
 
+def create_mock_pushblock_brain():
+    mock_brain = create_mock_brainparams(
+        vector_action_space_type="discrete",
+        vector_action_space_size=[7],
+        vector_observation_space_size=70,
+    )
+    mock_brain.brain_name = "PushblockLearning"
+    return mock_brain
+
+
 def create_mock_banana_brain():
     mock_brain = create_mock_brainparams(
         number_visual_observations=1,

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

 import os
+import yaml
+import pytest
+from mlagents.trainers.tests.test_bc import create_bc_trainer
 
 
 def test_barracuda_converter():
 
     # cleanup
     os.remove(tmpfile)
+
+
+@pytest.fixture
+def bc_dummy_config():
+    return yaml.safe_load(
+        """
+            hidden_units: 32
+            learning_rate: 3.0e-4
+            num_layers: 1
+            use_recurrent: false
+            sequence_length: 32
+            memory_size: 64
+            batches_per_epoch: 1
+            batch_size: 64
+            summary_freq: 2000
+            max_steps: 4000
+            """
+    )
+
+
+@pytest.mark.parametrize("use_lstm", [False, True], ids=["nolstm", "lstm"])
+@pytest.mark.parametrize("use_discrete", [True, False], ids=["disc", "cont"])
+def test_bc_export(bc_dummy_config, use_lstm, use_discrete):
+    bc_dummy_config["use_recurrent"] = use_lstm
+    trainer, env = create_bc_trainer(bc_dummy_config, use_discrete)
+    trainer.export_model()

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

     )
 
 
-def create_bc_trainer(dummy_config):
+def create_bc_trainer(dummy_config, is_discrete=False):
-    mock_brain = mb.create_mock_3dball_brain()
-    mock_braininfo = mb.create_mock_braininfo(num_agents=12, num_vector_observations=8)
+    if is_discrete:
+        mock_brain = mb.create_mock_pushblock_brain()
+        mock_braininfo = mb.create_mock_braininfo(
+            num_agents=12, num_vector_observations=70
+        )
+    else:
+        mock_brain = mb.create_mock_3dball_brain()
+        mock_braininfo = mb.create_mock_braininfo(
+            num_agents=12, num_vector_observations=8
+        )
     mb.setup_mock_unityenvironment(mock_env, mock_brain, mock_braininfo)
     env = mock_env()