Merge branch 'internal-policy-ghost' into soccer-2v1

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

     )
 
     # first policy encountered becomes policy trained by wrapped PPO
-    policy = trainer.create_policy(brain_params_team0)
-    trainer.add_policy(parsed_behavior_id0, policy)
+    trainer.add_policy(parsed_behavior_id0, brain_params_team0)
-    policy = trainer.create_policy(brain_params_team1)
-    trainer.add_policy(parsed_behavior_id1, policy)
+    trainer.add_policy(parsed_behavior_id1, brain_params_team1)
     trajectory_queue1 = AgentManagerQueue(brain_params_team1.brain_name)
     trainer.subscribe_trajectory_queue(trajectory_queue1)
 
 
     # First policy encountered becomes policy trained by wrapped PPO
     # This queue should remain empty after swap snapshot
-    policy = trainer.create_policy(brain_params_team0)
-    trainer.add_policy(parsed_behavior_id0, policy)
+    trainer.add_policy(parsed_behavior_id0, brain_params_team0)
-    policy = trainer.create_policy(brain_params_team1)
-    trainer.add_policy(parsed_behavior_id1, policy)
+    trainer.add_policy(parsed_behavior_id1, brain_params_team1)
     policy_queue1 = AgentManagerQueue(brain_params_team1.brain_name)
     trainer.publish_policy_queue(policy_queue1)
 

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

     )
 
 
+@mock.patch("mlagents.trainers.ppo.trainer.NNPolicy")
-def test_trainer_increment_step(ppo_optimizer, dummy_config):
+def test_trainer_increment_step(ppo_optimizer, nn_policy, dummy_config):
+
+    mock_policy = mock.Mock()
+    mock_policy.get_current_step = mock.Mock(return_value=0)
+    step_count = (
+        5
+    )  # 10 hacked because this function is no longer called through trainer
+
+    mock_policy.increment_step = mock.Mock(return_value=step_count)
+    nn_policy.return_value = mock_policy
 
     brain_params = BrainParameters(
         brain_name="test_brain",
     trainer = PPOTrainer(
         brain_params.brain_name, 0, trainer_params, True, False, 0, "0"
     )
-    policy_mock = mock.Mock(spec=NNPolicy)
-    policy_mock.get_current_step.return_value = 0
-    step_count = (
-        5
-    )  # 10 hacked because this function is no longer called through trainer
-    policy_mock.increment_step = mock.Mock(return_value=step_count)
-    trainer.add_policy("testbehavior", policy_mock)
+    trainer.add_policy("testbehavior", brain_params)
+    policy = trainer.get_policy("testbehavior")
-    policy_mock.increment_step.assert_called_with(5)
+    policy.increment_step.assert_called_with(5)
 
 
 @pytest.mark.parametrize("use_discrete", [True, False])
     trainer_params["reward_signals"]["curiosity"]["encoding_size"] = 128
 
     trainer = PPOTrainer(mock_brain.brain_name, 0, trainer_params, True, False, 0, "0")
-    policy = trainer.create_policy(mock_brain)
-    trainer.add_policy(mock_brain.brain_name, policy)
+    trainer.add_policy(mock_brain.brain_name, mock_brain)
     # Test update with sequence length smaller than batch size
     buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, mock_brain)
     # Mock out reward signal eval
     dummy_config["summary_path"] = "./summaries/test_trainer_summary"
     dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
     trainer = PPOTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
-    policy = trainer.create_policy(brain_params)
-    trainer.add_policy(brain_params.brain_name, policy)
+    trainer.add_policy(brain_params.brain_name, brain_params)
     trajectory_queue = AgentManagerQueue("testbrain")
     trainer.subscribe_trajectory_queue(trajectory_queue)
     time_horizon = 15
     assert trainer.stats_reporter.get_stats_summaries("Policy/Extrinsic Reward").num > 0
 
 
+@mock.patch("mlagents.trainers.ppo.trainer.NNPolicy")
-def test_add_get_policy(ppo_optimizer, dummy_config):
+def test_add_get_policy(ppo_optimizer, nn_policy, dummy_config):
     brain_params = make_brain_parameters(
         discrete_action=False, visual_inputs=0, vec_obs_size=6
     )
 
+    mock_policy = mock.Mock()
+    mock_policy.get_current_step = mock.Mock(return_value=2000)
+    nn_policy.return_value = mock_policy
+
-    policy = mock.Mock(spec=NNPolicy)
-    policy.get_current_step.return_value = 2000
-
-    trainer.add_policy(brain_params.brain_name, policy)
-    assert trainer.get_policy(brain_params.brain_name) == policy
+    trainer.add_policy(brain_params.brain_name, brain_params)
-
-    # Test incorrect class of policy
-    policy = mock.Mock()
-    with pytest.raises(RuntimeError):
-        trainer.add_policy(brain_params, policy)
 
 
 def test_bad_config(dummy_config):

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

     trainer_params["model_path"] = str(tmpdir)
     trainer_params["save_replay_buffer"] = True
     trainer = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, False, 0, 0)
-    policy = trainer.create_policy(mock_brain)
-    trainer.add_policy(mock_brain.brain_name, policy)
+    trainer.add_policy(mock_brain.brain_name, mock_brain)
+    policy = trainer.get_policy(mock_brain.brain_name)
 
     trainer.update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, policy.brain)
     buffer_len = trainer.update_buffer.num_experiences
     trainer2 = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, True, 0, 0)
 
-    policy = trainer2.create_policy(mock_brain)
-    trainer2.add_policy(mock_brain.brain_name, policy)
+    trainer2.add_policy(mock_brain.brain_name, mock_brain)
+@mock.patch("mlagents.trainers.sac.trainer.NNPolicy")
-def test_add_get_policy(sac_optimizer, dummy_config):
+def test_add_get_policy(sac_optimizer, nn_policy, dummy_config):
     brain_params = make_brain_parameters(
         discrete_action=False, visual_inputs=0, vec_obs_size=6
     )
 
+    mock_policy = mock.Mock()
+    mock_policy.get_current_step = mock.Mock(return_value=2000)
+    nn_policy.return_value = mock_policy
-    trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
-    policy = mock.Mock(spec=NNPolicy)
-    policy.get_current_step.return_value = 2000
-    trainer.add_policy(brain_params.brain_name, policy)
-    assert trainer.get_policy(brain_params.brain_name) == policy
+    trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
+    trainer.add_policy(brain_params.brain_name, brain_params)
-    # Test incorrect class of policy
-    policy = mock.Mock()
-    with pytest.raises(RuntimeError):
-        trainer.add_policy(brain_params, policy)
-
 
 def test_process_trajectory(dummy_config):
     brain_params = make_brain_parameters(
     dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
     trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
-    policy = trainer.create_policy(brain_params)
-    trainer.add_policy(brain_params.brain_name, policy)
+    trainer.add_policy(brain_params.brain_name, brain_params)
 
     trajectory_queue = AgentManagerQueue("testbrain")
     trainer.subscribe_trajectory_queue(trajectory_queue)