def test_bcmodule_update(is_sac):
mock_brain = mb.create_mock_3dball_brain()
bc_module = create_bc_module(mock_brain, ppo_dummy_config(), False,
"test.demo", is_sac)
stats = bc_module.update()
for _, item in stats.items():
assert isinstance(item, np.float32)
def create_bc_trainer(dummy_config, is_discrete=False):
mock_env = mock.Mock()
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()
trainer_parameters = dummy_config
trainer_parameters["summary_path"] = "tmp"
trainer_parameters["model_path"] = "tmp"
trainer_parameters["demo_path"] = (
os.path.dirname(os.path.abspath(__file__)) + "/test.demo")
trainer = BCTrainer(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_bcmodule_rnn_update(trainer_config):
mock_brain = mb.create_mock_3dball_brain()
policy = create_policy_with_bc_mock(mock_brain, trainer_config, True,
"test.demo")
stats = policy.bc_module.update()
for _, item in stats.items():
assert isinstance(item, np.float32)
def test_bcmodule_update(mock_env, trainer_config):
mock_brain = mb.create_mock_3dball_brain()
env, policy = create_policy_with_bc_mock(mock_env, mock_brain,
trainer_config, False,
"test.demo")
stats = policy.bc_module.update()
for _, item in stats.items():
assert isinstance(item, np.float32)
env.close()
def test_bcmodule_rnn_update(is_sac):
mock_brain = mb.create_mock_3dball_brain()
bc_settings = BehavioralCloningSettings(
demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" +
"test.demo")
bc_module = create_bc_module(mock_brain, bc_settings, True, is_sac)
stats = bc_module.update()
for _, item in stats.items():
assert isinstance(item, np.float32)
def test_bcmodule_constant_lr_update(trainer_config):
mock_brain = mb.create_mock_3dball_brain()
trainer_config["behavioral_cloning"]["steps"] = 0
policy = create_policy_with_bc_mock(mock_brain, trainer_config, False, "test.demo")
stats = policy.bc_module.update()
for _, item in stats.items():
assert isinstance(item, np.float32)
old_learning_rate = policy.bc_module.current_lr
stats = policy.bc_module.update()
assert old_learning_rate == policy.bc_module.current_lr
def test_bcmodule_constant_lr_update(is_sac):
trainer_config = ppo_dummy_config()
mock_brain = mb.create_mock_3dball_brain()
trainer_config["behavioral_cloning"]["steps"] = 0
bc_module = create_bc_module(mock_brain, trainer_config, False, "test.demo", is_sac)
stats = bc_module.update()
for _, item in stats.items():
assert isinstance(item, np.float32)
old_learning_rate = bc_module.current_lr
stats = bc_module.update()
assert old_learning_rate == bc_module.current_lr
def test_bcmodule_defaults():
# See if default values match
mock_brain = mb.create_mock_3dball_brain()
trainer_config = ppo_dummy_config()
policy = create_policy_with_bc_mock(mock_brain, trainer_config, False, "test.demo")
assert policy.bc_module.num_epoch == 3
assert policy.bc_module.batch_size == trainer_config["batch_size"]
# Assign strange values and see if it overrides properly
trainer_config["behavioral_cloning"]["num_epoch"] = 100
trainer_config["behavioral_cloning"]["batch_size"] = 10000
policy = create_policy_with_bc_mock(mock_brain, trainer_config, False, "test.demo")
assert policy.bc_module.num_epoch == 100
assert policy.bc_module.batch_size == 10000
def test_bcmodule_constant_lr_update(is_sac):
mock_brain = mb.create_mock_3dball_brain()
bc_settings = BehavioralCloningSettings(
demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" +
"test.demo",
steps=0,
)
bc_module = create_bc_module(mock_brain, bc_settings, False, is_sac)
stats = bc_module.update()
for _, item in stats.items():
assert isinstance(item, np.float32)
old_learning_rate = bc_module.current_lr
stats = bc_module.update()
assert old_learning_rate == bc_module.current_lr
def test_bcmodule_defaults(mock_env, dummy_config):
# See if default values match
mock_brain = mb.create_mock_3dball_brain()
env, policy = create_ppo_policy_with_bc_mock(mock_env, mock_brain,
dummy_config, False,
"test.demo")
assert policy.bc_module.num_epoch == dummy_config["num_epoch"]
assert policy.bc_module.batch_size == dummy_config["batch_size"]
env.close()
# Assign strange values and see if it overrides properly
dummy_config["pretraining"]["num_epoch"] = 100
dummy_config["pretraining"]["batch_size"] = 10000
env, policy = create_ppo_policy_with_bc_mock(mock_env, mock_brain,
dummy_config, False,
"test.demo")
assert policy.bc_module.num_epoch == 100
assert policy.bc_module.batch_size == 10000
env.close()
def test_bcmodule_defaults():
# See if default values match
mock_brain = mb.create_mock_3dball_brain()
bc_settings = BehavioralCloningSettings(
demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" +
"test.demo")
bc_module = create_bc_module(mock_brain, bc_settings, False, False)
assert bc_module.num_epoch == 3
assert bc_module.batch_size == TrainerSettings().hyperparameters.batch_size
# Assign strange values and see if it overrides properly
bc_settings = BehavioralCloningSettings(
demo_path=os.path.dirname(os.path.abspath(__file__)) + "/" +
"test.demo",
num_epoch=100,
batch_size=10000,
)
bc_module = create_bc_module(mock_brain, bc_settings, False, False)
assert bc_module.num_epoch == 100
assert bc_module.batch_size == 10000
def test_bc_trainer(mock_env, dummy_config):
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()
trainer_parameters = dummy_config
trainer_parameters["summary_path"] = "tmp"
trainer_parameters["model_path"] = "tmp"
trainer_parameters["demo_path"] = (
os.path.dirname(os.path.abspath(__file__)) + "/test.demo")
trainer = BCTrainer(mock_brain,
trainer_parameters,
training=True,
load=False,
seed=0,
run_id=0)
trainer.demonstration_buffer = mb.simulate_rollout(env, trainer.policy,
100)
trainer.update_policy()
assert len(trainer.stats["Losses/Cloning Loss"]) > 0
trainer.increment_step(1)
assert trainer.step == 1
