Add more extensive tests for BC trainer (#2506)

* Add more extensive tests for BC trainer
* Break up tests for BC trainer

Author: Ervin T

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

@@ -91,12 +91,13 @@ def setup_mock_unityenvironment(mock_env, mock_brain, mock_braininfo):
     :Mock mock_brain: A mock Brain object that specifies the params of this environment.
     :Mock mock_braininfo: A mock BrainInfo object that will be returned at each step and reset.
     """
+    brain_name = mock_brain.brain_name
     mock_env.return_value.academy_name = "MockAcademy"
-    mock_env.return_value.brains = {"MockBrain": mock_brain}
-    mock_env.return_value.external_brain_names = ["MockBrain"]
-    mock_env.return_value.brain_names = ["MockBrain"]
-    mock_env.return_value.reset.return_value = {"MockBrain": mock_braininfo}
-    mock_env.return_value.step.return_value = {"MockBrain": mock_braininfo}
+    mock_env.return_value.brains = {brain_name: mock_brain}
+    mock_env.return_value.external_brain_names = [brain_name]
+    mock_env.return_value.brain_names = [brain_name]
+    mock_env.return_value.reset.return_value = {brain_name: mock_braininfo}
+    mock_env.return_value.step.return_value = {brain_name: mock_braininfo}
 
 
 def simulate_rollout(env, policy, buffer_init_samples):

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

@@ -18,9 +18,9 @@
 def dummy_config():
     return yaml.safe_load(
         """
-            hidden_units: 128
+            hidden_units: 32
             learning_rate: 3.0e-4
-            num_layers: 2
+            num_layers: 1
             use_recurrent: false
             sequence_length: 32
             memory_size: 32
@@ -32,8 +32,8 @@ def dummy_config():
     )
 
 
-@mock.patch("mlagents.envs.UnityEnvironment")
-def test_bc_trainer(mock_env, dummy_config):
+def create_bc_trainer(dummy_config):
+    mock_env = mock.Mock()
     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)
@@ -49,12 +49,54 @@ def test_bc_trainer(mock_env, dummy_config):
         mock_brain, trainer_parameters, training=True, load=False, seed=0, run_id=0
     )
     trainer.demonstration_buffer = mb.simulate_rollout(env, trainer.policy, 100)
+    return trainer, env
+
+
+def test_bc_trainer_step(dummy_config):
+    trainer, env = create_bc_trainer(dummy_config)
+    # Test get_step
+    assert trainer.get_step == 0
+    # Test update policy
     trainer.update_policy()
     assert len(trainer.stats["Losses/Cloning Loss"]) > 0
+    # Test increment step
     trainer.increment_step(1)
     assert trainer.step == 1
 
 
+def test_bc_trainer_add_proc_experiences(dummy_config):
+    trainer, env = create_bc_trainer(dummy_config)
+    # Test add_experiences
+    returned_braininfo = env.step()
+    trainer.add_experiences(
+        returned_braininfo, returned_braininfo, {}
+    )  # Take action outputs is not used
+    for agent_id in returned_braininfo["Ball3DBrain"].agents:
+        assert trainer.evaluation_buffer[agent_id].last_brain_info is not None
+        assert trainer.episode_steps[agent_id] > 0
+        assert trainer.cumulative_rewards[agent_id] > 0
+    # Test process_experiences by setting done
+    returned_braininfo["Ball3DBrain"].local_done = 12 * [True]
+    trainer.process_experiences(returned_braininfo, returned_braininfo)
+    for agent_id in returned_braininfo["Ball3DBrain"].agents:
+        assert trainer.episode_steps[agent_id] == 0
+        assert trainer.cumulative_rewards[agent_id] == 0
+
+
+def test_bc_trainer_end_episode(dummy_config):
+    trainer, env = create_bc_trainer(dummy_config)
+    returned_braininfo = env.step()
+    trainer.add_experiences(
+        returned_braininfo, returned_braininfo, {}
+    )  # Take action outputs is not used
+    trainer.process_experiences(returned_braininfo, returned_braininfo)
+    # Should set everything to 0
+    trainer.end_episode()
+    for agent_id in returned_braininfo["Ball3DBrain"].agents:
+        assert trainer.episode_steps[agent_id] == 0
+        assert trainer.cumulative_rewards[agent_id] == 0
+
+
 @mock.patch("mlagents.envs.UnityEnvironment.executable_launcher")
 @mock.patch("mlagents.envs.UnityEnvironment.get_communicator")
 def test_bc_policy_evaluate(mock_communicator, mock_launcher, dummy_config):