def test_dqfd(self):
# Create a fake environment to test with.
# TODO(b/152596848): Allow DQN to deal with integer observations.
environment = fakes.DiscreteEnvironment(num_actions=5,
num_observations=10,
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
# Build demonstrations.
dummy_action = np.zeros((), dtype=np.int32)
recorder = bsuite_demonstrations.DemonstrationRecorder()
timestep = environment.reset()
while timestep.step_type is not dm_env.StepType.LAST:
recorder.step(timestep, dummy_action)
timestep = environment.step(dummy_action)
recorder.step(timestep, dummy_action)
recorder.record_episode()
# Construct the agent.
agent = dqfd.DQfD(environment_spec=spec,
network=_make_network(spec.actions),
demonstration_dataset=recorder.make_tf_dataset(),
demonstration_ratio=0.5,
batch_size=10,
samples_per_insert=2,
min_replay_size=10)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent)
loop.run(num_episodes=10)
def test_mcts(self):
# Create a fake environment to test with.
num_actions = 5
environment = fakes.DiscreteEnvironment(num_actions=num_actions,
num_observations=10,
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
network = snt.Sequential([
snt.Flatten(),
snt.nets.MLP([50, 50]),
networks.PolicyValueHead(spec.actions.num_values),
])
model = simulator.Simulator(environment)
optimizer = snt.optimizers.Adam(1e-3)
# Construct the agent.
agent = mcts.MCTS(environment_spec=spec,
network=network,
model=model,
optimizer=optimizer,
n_step=1,
discount=1.,
replay_capacity=100,
num_simulations=10,
batch_size=10)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent)
loop.run(num_episodes=2)
def test_dqn(self):
# Create a fake environment to test with.
environment = fakes.DiscreteEnvironment(num_actions=5,
num_observations=10,
obs_shape=(10, 5),
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
def network(x):
model = hk.Sequential(
[hk.Flatten(),
hk.nets.MLP([50, 50, spec.actions.num_values])])
return model(x)
# Make network purely functional
network_hk = hk.without_apply_rng(hk.transform(network,
apply_rng=True))
dummy_obs = utils.add_batch_dim(utils.zeros_like(spec.observations))
network = networks_lib.FeedForwardNetwork(
init=lambda rng: network_hk.init(rng, dummy_obs),
apply=network_hk.apply)
# Construct the agent.
agent = dqn.DQN(environment_spec=spec,
network=network,
batch_size=10,
samples_per_insert=2,
min_replay_size=10)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent)
loop.run(num_episodes=20)
def test_reset(self):
"""Ensure that noop starts `reset` steps the environment multiple times."""
noop_action = 0
noop_max = 10
seed = 24
base_env = fakes.DiscreteEnvironment(action_dtype=np.int64,
obs_dtype=np.int64,
reward_spec=specs.Array(
dtype=np.float64, shape=()))
mock_step_fn = mock.MagicMock()
expected_num_step_calls = np.random.RandomState(seed).randint(
noop_max + 1)
with mock.patch.object(base_env, 'step', mock_step_fn):
env = wrappers.NoopStartsWrapper(
base_env,
noop_action=noop_action,
noop_max=noop_max,
seed=seed,
)
env.reset()
# Test environment step called with noop action as part of wrapper.reset
mock_step_fn.assert_called_with(noop_action)
self.assertEqual(mock_step_fn.call_count, expected_num_step_calls)
self.assertEqual(mock_step_fn.call_args, ((noop_action, ), {}))
def run_ppo_agent(self, make_networks_fn):
# Create a fake environment to test with.
environment = fakes.DiscreteEnvironment(num_actions=5,
num_observations=10,
obs_shape=(10, 5),
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
distribution_value_networks = make_networks_fn(spec)
ppo_networks = ppo.make_ppo_networks(distribution_value_networks)
config = ppo.PPOConfig(unroll_length=4,
num_epochs=2,
num_minibatches=2)
workdir = self.create_tempdir()
counter = counting.Counter()
logger = loggers.make_default_logger('learner')
# Construct the agent.
agent = ppo.PPO(
spec=spec,
networks=ppo_networks,
config=config,
seed=0,
workdir=workdir.full_path,
normalize_input=True,
counter=counter,
logger=logger,
)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent, counter=counter)
loop.run(num_episodes=20)
def test_impala(self):
# Create a fake environment to test with.
environment = fakes.DiscreteEnvironment(num_actions=5,
num_observations=10,
obs_shape=(10, 5),
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
def network(x, s):
model = MyNetwork(spec.actions.num_values)
return model(x, s)
def initial_state_fn(batch_size: Optional[int] = None):
model = MyNetwork(spec.actions.num_values)
return model.initial_state(batch_size)
# Construct the agent.
agent = impala.IMPALA(
environment_spec=spec,
network=network,
initial_state_fn=initial_state_fn,
sequence_length=3,
sequence_period=3,
batch_size=6,
)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent)
loop.run(num_episodes=20)
def test_r2d2(self):
# Create a fake environment to test with.
# TODO(b/152596848): Allow R2D2 to deal with integer observations.
environment = fakes.DiscreteEnvironment(num_actions=5,
num_observations=10,
obs_shape=(10, 4),
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
# Construct the agent.
agent = r2d2.R2D2(
environment_spec=spec,
network=SimpleNetwork(spec.actions),
batch_size=10,
samples_per_insert=2,
min_replay_size=10,
burn_in_length=2,
trace_length=6,
replay_period=4,
checkpoint=False,
)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent)
loop.run(num_episodes=5)
def test_dqn(self):
# Create a fake environment to test with.
environment = fakes.DiscreteEnvironment(num_actions=5,
num_observations=10,
obs_shape=(10, 5),
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
def network(x):
model = hk.Sequential(
[hk.Flatten(),
hk.nets.MLP([50, 50, spec.actions.num_values])])
return model(x)
# Construct the agent.
agent = dqn.DQN(environment_spec=spec,
network=network,
batch_size=10,
samples_per_insert=2,
min_replay_size=10)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent)
loop.run(num_episodes=20)
def test_environment_loop(self):
# Create the actor/environment and stick them in a loop.
environment = fakes.DiscreteEnvironment(episode_length=10)
actor = fakes.Actor(specs.make_environment_spec(environment))
loop = environment_loop.EnvironmentLoop(environment, actor)
# Run the loop. There should be episode_length+1 update calls per episode.
loop.run(num_episodes=10)
self.assertEqual(actor.num_updates, 100)
def test_pickle_unpickle(self):
test_env = base.EnvironmentWrapper(
environment=fakes.DiscreteEnvironment())
test_env_pickled = pickle.dumps(test_env)
test_env_restored = pickle.loads(test_env_pickled)
self.assertEqual(
test_env.observation_spec(),
test_env_restored.observation_spec(),
)
def test_raises_value_error(self):
"""Ensure that wrapper raises error if noop_max is <0."""
base_env = fakes.DiscreteEnvironment(action_dtype=np.int64,
obs_dtype=np.int64,
reward_spec=specs.Array(
dtype=np.float64, shape=()))
with self.assertRaises(ValueError):
wrappers.NoopStartsWrapper(base_env,
noop_action=0,
noop_max=-1,
seed=24)
def test_discrete_actions(self, loss_name):
with chex.fake_pmap_and_jit():
num_sgd_steps_per_step = 1
num_steps = 5
# Create a fake environment to test with.
environment = fakes.DiscreteEnvironment(num_actions=10,
num_observations=100,
obs_shape=(10, ),
obs_dtype=np.float32)
spec = specs.make_environment_spec(environment)
dataset_demonstration = fakes.transition_dataset(environment)
dataset_demonstration = dataset_demonstration.map(
lambda sample: types.Transition(*sample.data))
dataset_demonstration = dataset_demonstration.batch(
8).as_numpy_iterator()
# Construct the agent.
network = make_networks(spec, discrete_actions=True)
def logp_fn(logits, actions):
max_logits = jnp.max(logits, axis=-1, keepdims=True)
logits = logits - max_logits
logits_actions = jnp.sum(
jax.nn.one_hot(actions, spec.actions.num_values) * logits,
axis=-1)
log_prob = logits_actions - special.logsumexp(logits, axis=-1)
return log_prob
if loss_name == 'logp':
loss_fn = bc.logp(logp_fn=logp_fn)
elif loss_name == 'rcal':
base_loss_fn = bc.logp(logp_fn=logp_fn)
loss_fn = bc.rcal(base_loss_fn, discount=0.99, alpha=0.1)
else:
raise ValueError
learner = bc.BCLearner(
network=network,
random_key=jax.random.PRNGKey(0),
loss_fn=loss_fn,
optimizer=optax.adam(0.01),
demonstrations=dataset_demonstration,
num_sgd_steps_per_step=num_sgd_steps_per_step)
# Train the agent
for _ in range(num_steps):
learner.step()
def test_impala(self):
# Create a fake environment to test with.
environment = fakes.DiscreteEnvironment(num_actions=5,
num_observations=10,
obs_shape=(10, 5),
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
def forward_fn(x, s):
model = MyNetwork(spec.actions.num_values)
return model(x, s)
def initial_state_fn(batch_size: Optional[int] = None):
model = MyNetwork(spec.actions.num_values)
return model.initial_state(batch_size)
def unroll_fn(inputs, state):
model = MyNetwork(spec.actions.num_values)
return hk.static_unroll(model, inputs, state)
# We pass pure, Haiku-agnostic functions to the agent.
forward_fn_transformed = hk.without_apply_rng(
hk.transform(forward_fn, apply_rng=True))
unroll_fn_transformed = hk.without_apply_rng(
hk.transform(unroll_fn, apply_rng=True))
initial_state_fn_transformed = hk.without_apply_rng(
hk.transform(initial_state_fn, apply_rng=True))
# Construct the agent.
config = impala_agent.IMPALAConfig(
sequence_length=3,
sequence_period=3,
batch_size=6,
)
agent = impala.IMPALAFromConfig(
environment_spec=spec,
forward_fn=forward_fn_transformed.apply,
initial_state_init_fn=initial_state_fn_transformed.init,
initial_state_fn=initial_state_fn_transformed.apply,
unroll_init_fn=unroll_fn_transformed.init,
unroll_fn=unroll_fn_transformed.apply,
config=config,
)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent)
loop.run(num_episodes=20)
def test_discrete(self):
env = wrappers.SinglePrecisionWrapper(
fakes.DiscreteEnvironment(
action_dtype=np.int64, obs_dtype=np.int64, reward_dtype=np.float64))
self.assertTrue(np.issubdtype(env.observation_spec().dtype, np.int32))
self.assertTrue(np.issubdtype(env.action_spec().dtype, np.int32))
self.assertTrue(np.issubdtype(env.reward_spec().dtype, np.float32))
self.assertTrue(np.issubdtype(env.discount_spec().dtype, np.float32))
timestep = env.reset()
self.assertEqual(timestep.reward, None)
self.assertEqual(timestep.discount, None)
self.assertTrue(np.issubdtype(timestep.observation.dtype, np.int32))
timestep = env.step(0)
self.assertTrue(np.issubdtype(timestep.reward.dtype, np.float32))
self.assertTrue(np.issubdtype(timestep.discount.dtype, np.float32))
self.assertTrue(np.issubdtype(timestep.observation.dtype, np.int32))
def test_dqn(self):
# Create a fake environment to test with.
environment = fakes.DiscreteEnvironment(num_actions=5,
num_observations=10,
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
# Construct the agent.
agent = dqn.DQN(environment_spec=spec,
network=_make_network(spec.actions),
batch_size=10,
samples_per_insert=2,
min_replay_size=10)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent)
loop.run(num_episodes=2)
def _parameterized_setup(discount_spec: Optional[types.NestedSpec] = None,
reward_spec: Optional[types.NestedSpec] = None):
"""Common setup code that, unlike self.setUp, takes arguments.
Args:
discount_spec: None, or a (nested) specs.BoundedArray.
reward_spec: None, or a (nested) specs.Array.
Returns:
environment, actor, loop
"""
env_kwargs = {'episode_length': EPISODE_LENGTH}
if discount_spec:
env_kwargs['discount_spec'] = discount_spec
if reward_spec:
env_kwargs['reward_spec'] = reward_spec
environment = fakes.DiscreteEnvironment(**env_kwargs)
actor = fakes.Actor(specs.make_environment_spec(environment))
loop = environment_loop.EnvironmentLoop(environment, actor)
return actor, loop
def test_impala(self):
# Create a fake environment to test with.
environment = fakes.DiscreteEnvironment(num_actions=5,
num_observations=10,
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
# Construct the agent.
agent = impala.IMPALA(
environment_spec=spec,
network=_make_network(spec.actions),
sequence_length=3,
sequence_period=3,
batch_size=6,
)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent)
loop.run(num_episodes=20)
def test_full_learner(self):
# Create dataset.
environment = fakes.DiscreteEnvironment(
num_actions=5,
num_observations=10,
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
dataset = fakes.transition_dataset(environment).batch(
2, drop_remainder=True)
# Build network.
network = networks.IQNNetwork(
torso=_make_torso_network(num_outputs=2),
head=_make_head_network(num_outputs=spec.actions.num_values),
latent_dim=2,
num_quantile_samples=1)
tf2_utils.create_variables(network, [spec.observations])
# Build learner.
counter = counting.Counter()
learner = iqn.IQNLearner(
network=network,
target_network=copy.deepcopy(network),
dataset=dataset,
learning_rate=1e-4,
discount=0.99,
importance_sampling_exponent=0.2,
target_update_period=1,
counter=counter)
# Run a learner step.
learner.step()
# Check counts from IQN learner.
counts = counter.get_counts()
self.assertEqual(1, counts['steps'])
# Check learner state.
self.assertEqual(1, learner.state['num_steps'].numpy())
def test_full_learner(self):
# Create dataset.
environment = fakes.DiscreteEnvironment(
num_actions=5,
num_observations=10,
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
dataset = fakes.transition_dataset(environment).batch(2)
# Build network.
g_network = _make_network(spec.actions)
q_network = _make_network(spec.actions)
network = discrete_networks.DiscreteFilteredQNetwork(g_network=g_network,
q_network=q_network,
threshold=0.5)
tf2_utils.create_variables(network, [spec.observations])
# Build learner.
counter = counting.Counter()
learner = bcq.DiscreteBCQLearner(
network=network,
dataset=dataset,
learning_rate=1e-4,
discount=0.99,
importance_sampling_exponent=0.2,
target_update_period=100,
counter=counter)
# Run a learner step.
learner.step()
# Check counts from BC and BCQ learners.
counts = counter.get_counts()
self.assertEqual(1, counts['bc_steps'])
self.assertEqual(1, counts['bcq_steps'])
# Check learner state.
self.assertEqual(1, learner.state['bc_num_steps'].numpy())
self.assertEqual(1, learner.state['bcq_num_steps'].numpy())
def test_r2d3(self):
# Create a fake environment to test with.
environment = fakes.DiscreteEnvironment(num_actions=5,
num_observations=10,
obs_dtype=np.float32,
episode_length=10)
spec = specs.make_environment_spec(environment)
# Build demonstrations.
dummy_action = np.zeros((), dtype=np.int32)
recorder = bsuite_demonstrations.DemonstrationRecorder()
timestep = environment.reset()
while timestep.step_type is not dm_env.StepType.LAST:
recorder.step(timestep, dummy_action)
timestep = environment.step(dummy_action)
recorder.step(timestep, dummy_action)
recorder.record_episode()
# Construct the agent.
agent = r2d3.R2D3(
environment_spec=spec,
network=SimpleNetwork(spec.actions),
target_network=SimpleNetwork(spec.actions),
demonstration_dataset=recorder.make_tf_dataset(),
demonstration_ratio=0.5,
batch_size=10,
samples_per_insert=2,
min_replay_size=10,
burn_in_length=2,
trace_length=6,
replay_period=4,
checkpoint=False,
)
# Try running the environment loop. We have no assertions here because all
# we care about is that the agent runs without raising any errors.
loop = acme.EnvironmentLoop(environment, agent)
loop.run(num_episodes=5)
def test_ail(self,
algo,
airl_discriminator=False,
subtract_logpi=False,
dropout=0.,
lipschitz_coeff=None):
shutil.rmtree(flags.FLAGS.test_tmpdir, ignore_errors=True)
batch_size = 8
# Mujoco environment and associated demonstration dataset.
if algo == 'ppo':
environment = fakes.DiscreteEnvironment(
num_actions=NUM_DISCRETE_ACTIONS,
num_observations=NUM_OBSERVATIONS,
obs_shape=OBS_SHAPE,
obs_dtype=OBS_DTYPE,
episode_length=EPISODE_LENGTH)
else:
environment = fakes.ContinuousEnvironment(
episode_length=EPISODE_LENGTH,
action_dim=CONTINUOUS_ACTION_DIM,
observation_dim=CONTINUOUS_OBS_DIM,
bounded=True)
spec = specs.make_environment_spec(environment)
if algo == 'sac':
networks = sac.make_networks(spec=spec)
config = sac.SACConfig(
batch_size=batch_size,
samples_per_insert_tolerance_rate=float('inf'),
min_replay_size=1)
base_builder = sac.SACBuilder(config=config)
direct_rl_batch_size = batch_size
behavior_policy = sac.apply_policy_and_sample(networks)
elif algo == 'ppo':
unroll_length = 5
distribution_value_networks = make_ppo_networks(spec)
networks = ppo.make_ppo_networks(distribution_value_networks)
config = ppo.PPOConfig(unroll_length=unroll_length,
num_minibatches=2,
num_epochs=4,
batch_size=batch_size)
base_builder = ppo.PPOBuilder(config=config)
direct_rl_batch_size = batch_size * unroll_length
behavior_policy = jax.jit(ppo.make_inference_fn(networks),
backend='cpu')
else:
raise ValueError(f'Unexpected algorithm {algo}')
if subtract_logpi:
assert algo == 'sac'
logpi_fn = make_sac_logpi(networks)
else:
logpi_fn = None
if algo == 'ppo':
embedding = lambda x: jnp.reshape(x, list(x.shape[:-2]) + [-1])
else:
embedding = lambda x: x
def discriminator(*args, **kwargs) -> networks_lib.Logits:
if airl_discriminator:
return ail.AIRLModule(
environment_spec=spec,
use_action=True,
use_next_obs=True,
discount=.99,
g_core=ail.DiscriminatorMLP(
[4, 4],
hidden_dropout_rate=dropout,
spectral_normalization_lipschitz_coeff=lipschitz_coeff
),
h_core=ail.DiscriminatorMLP(
[4, 4],
hidden_dropout_rate=dropout,
spectral_normalization_lipschitz_coeff=lipschitz_coeff
),
observation_embedding=embedding)(*args, **kwargs)
else:
return ail.DiscriminatorModule(
environment_spec=spec,
use_action=True,
use_next_obs=True,
network_core=ail.DiscriminatorMLP(
[4, 4],
hidden_dropout_rate=dropout,
spectral_normalization_lipschitz_coeff=lipschitz_coeff
),
observation_embedding=embedding)(*args, **kwargs)
discriminator_transformed = hk.without_apply_rng(
hk.transform_with_state(discriminator))
discriminator_network = ail.make_discriminator(
environment_spec=spec,
discriminator_transformed=discriminator_transformed,
logpi_fn=logpi_fn)
networks = ail.AILNetworks(discriminator_network, lambda x: x,
networks)
builder = ail.AILBuilder(
base_builder,
config=ail.AILConfig(
is_sequence_based=(algo == 'ppo'),
share_iterator=True,
direct_rl_batch_size=direct_rl_batch_size,
discriminator_batch_size=2,
policy_variable_name='policy' if subtract_logpi else None,
min_replay_size=1),
discriminator_loss=ail.losses.gail_loss(),
make_demonstrations=fakes.transition_iterator(environment))
# Construct the agent.
agent = local_layout.LocalLayout(seed=0,
environment_spec=spec,
builder=builder,
networks=networks,
policy_network=behavior_policy,
min_replay_size=1,
batch_size=batch_size)
# Train the agent.
train_loop = acme.EnvironmentLoop(environment, agent)
train_loop.run(num_episodes=(10 if algo == 'ppo' else 1))
def setUp(self):
super().setUp()
# Create the actor/environment and stick them in a loop.
environment = fakes.DiscreteEnvironment(episode_length=EPISODE_LENGTH)
self.actor = fakes.Actor(specs.make_environment_spec(environment))
self.loop = environment_loop.EnvironmentLoop(environment, self.actor)
def test_deepcopy(self):
test_env = base.EnvironmentWrapper(
environment=fakes.DiscreteEnvironment())
copied_env = copy.deepcopy(test_env)
del copied_env
