def test_bcq_impl(
observation_shape,
action_size,
actor_learning_rate,
critic_learning_rate,
imitator_learning_rate,
actor_optim_factory,
critic_optim_factory,
imitator_optim_factory,
encoder_factory,
q_func_factory,
gamma,
tau,
n_critics,
lam,
n_action_samples,
action_flexibility,
latent_size,
beta,
scaler,
action_scaler,
):
impl = BCQImpl(
observation_shape=observation_shape,
action_size=action_size,
actor_learning_rate=actor_learning_rate,
critic_learning_rate=critic_learning_rate,
imitator_learning_rate=imitator_learning_rate,
actor_optim_factory=actor_optim_factory,
critic_optim_factory=critic_optim_factory,
imitator_optim_factory=imitator_optim_factory,
actor_encoder_factory=encoder_factory,
critic_encoder_factory=encoder_factory,
imitator_encoder_factory=encoder_factory,
q_func_factory=create_q_func_factory(q_func_factory),
gamma=gamma,
tau=tau,
n_critics=n_critics,
lam=lam,
n_action_samples=n_action_samples,
action_flexibility=action_flexibility,
latent_size=latent_size,
beta=beta,
use_gpu=None,
scaler=scaler,
action_scaler=action_scaler,
)
impl.build()
# test internal methods
x = torch.rand(32, *observation_shape)
repeated_x = impl._repeat_observation(x)
assert repeated_x.shape == (32, n_action_samples) + observation_shape
action = impl._sample_repeated_action(repeated_x)
assert action.shape == (32, n_action_samples, action_size)
value = impl._predict_value(repeated_x, action)
assert value.shape == (n_critics, 32 * n_action_samples, 1)
best_action = impl._predict_best_action(x)
assert best_action.shape == (32, action_size)
torch_impl_tester(impl, discrete=False, deterministic_best_action=False)
def test_bcq_impl(
observation_shape,
action_size,
actor_learning_rate,
critic_learning_rate,
imitator_learning_rate,
actor_optim_factory,
critic_optim_factory,
imitator_optim_factory,
encoder_factory,
q_func_factory,
gamma,
tau,
n_critics,
bootstrap,
share_encoder,
lam,
n_action_samples,
action_flexibility,
latent_size,
beta,
scaler,
augmentation,
):
impl = BCQImpl(
observation_shape,
action_size,
actor_learning_rate,
critic_learning_rate,
imitator_learning_rate,
actor_optim_factory,
critic_optim_factory,
imitator_optim_factory,
encoder_factory,
encoder_factory,
encoder_factory,
create_q_func_factory(q_func_factory),
gamma,
tau,
n_critics,
bootstrap,
share_encoder,
lam,
n_action_samples,
action_flexibility,
latent_size,
beta,
use_gpu=False,
scaler=scaler,
augmentation=augmentation,
)
impl.build()
# test internal methods
x = torch.rand(32, *observation_shape)
repeated_x = impl._repeat_observation(x)
assert repeated_x.shape == (32, n_action_samples) + observation_shape
action = impl._sample_action(repeated_x)
assert action.shape == (32, n_action_samples, action_size)
value = impl._predict_value(repeated_x, action)
assert value.shape == (n_critics, 32 * n_action_samples, 1)
target = impl.compute_target(x)
if q_func_factory == "mean":
assert target.shape == (32, 1)
else:
assert target.shape == (32, impl._q_func.q_funcs[0]._n_quantiles)
best_action = impl._predict_best_action(x)
assert best_action.shape == (32, action_size)
torch_impl_tester(impl, discrete=False, deterministic_best_action=False)