def network_factory(spec: specs.EnvironmentSpec) -> ail.AILNetworks:
def discriminator(*args, **kwargs) -> networks_lib.Logits:
return ail.DiscriminatorModule(environment_spec=spec,
use_action=True,
use_next_obs=True,
network_core=ail.DiscriminatorMLP(
[4, 4], ))(*args, **kwargs)
discriminator_transformed = hk.without_apply_rng(
hk.transform_with_state(discriminator))
return ail.AILNetworks(ail.make_discriminator(
spec, discriminator_transformed),
imitation_reward_fn=ail.rewards.gail_reward(),
direct_rl_networks=sac.make_networks(spec))
def main(_):
# Create an environment, grab the spec, and use it to create networks.
environment = helpers.make_environment(task=FLAGS.env_name)
environment_spec = specs.make_environment_spec(environment)
# Construct the agent.
# Local layout makes sure that we populate the buffer with min_replay_size
# initial transitions and that there's no need for tolerance_rate. In order
# for deadlocks not to happen we need to disable rate limiting that heppens
# inside the TD3Builder. This is achieved by the min_replay_size and
# samples_per_insert_tolerance_rate arguments.
td3_config = td3.TD3Config(
num_sgd_steps_per_step=FLAGS.num_sgd_steps_per_step,
min_replay_size=1,
samples_per_insert_tolerance_rate=float('inf'))
td3_networks = td3.make_networks(environment_spec)
if FLAGS.pretrain:
td3_networks = add_bc_pretraining(td3_networks)
ail_config = ail.AILConfig(direct_rl_batch_size=td3_config.batch_size *
td3_config.num_sgd_steps_per_step)
dac_config = ail.DACConfig(ail_config, td3_config)
def discriminator(*args, **kwargs) -> networks_lib.Logits:
return ail.DiscriminatorModule(environment_spec=environment_spec,
use_action=True,
use_next_obs=True,
network_core=ail.DiscriminatorMLP(
[4, 4], ))(*args, **kwargs)
discriminator_transformed = hk.without_apply_rng(
hk.transform_with_state(discriminator))
ail_network = ail.AILNetworks(
ail.make_discriminator(environment_spec, discriminator_transformed),
imitation_reward_fn=ail.rewards.gail_reward(),
direct_rl_networks=td3_networks)
agent = ail.DAC(spec=environment_spec,
network=ail_network,
config=dac_config,
seed=FLAGS.seed,
batch_size=td3_config.batch_size *
td3_config.num_sgd_steps_per_step,
make_demonstrations=functools.partial(
helpers.make_demonstration_iterator,
dataset_name=FLAGS.dataset_name),
policy_network=td3.get_default_behavior_policy(
td3_networks,
action_specs=environment_spec.actions,
sigma=td3_config.sigma))
# Create the environment loop used for training.
train_logger = experiment_utils.make_experiment_logger(
label='train', steps_key='train_steps')
train_loop = acme.EnvironmentLoop(environment,
agent,
counter=counting.Counter(prefix='train'),
logger=train_logger)
# Create the evaluation actor and loop.
# TODO(lukstafi): sigma=0 for eval?
eval_logger = experiment_utils.make_experiment_logger(
label='eval', steps_key='eval_steps')
eval_actor = agent.builder.make_actor(
random_key=jax.random.PRNGKey(FLAGS.seed),
policy_network=td3.get_default_behavior_policy(
td3_networks, action_specs=environment_spec.actions, sigma=0.),
variable_source=agent)
eval_env = helpers.make_environment(task=FLAGS.env_name)
eval_loop = acme.EnvironmentLoop(eval_env,
eval_actor,
counter=counting.Counter(prefix='eval'),
logger=eval_logger)
assert FLAGS.num_steps % FLAGS.eval_every == 0
for _ in range(FLAGS.num_steps // FLAGS.eval_every):
eval_loop.run(num_episodes=5)
train_loop.run(num_steps=FLAGS.eval_every)
eval_loop.run(num_episodes=5)
def test_ail_flax(self):
shutil.rmtree(flags.FLAGS.test_tmpdir)
batch_size = 8
# Mujoco environment and associated demonstration dataset.
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)
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)
discriminator_module = DiscriminatorModule(spec, linen.Dense(1))
def apply_fn(params: networks_lib.Params,
policy_params: networks_lib.Params,
state: networks_lib.Params, transitions: types.Transition,
is_training: bool,
rng: networks_lib.PRNGKey) -> networks_lib.Logits:
del policy_params
variables = dict(params=params, **state)
return discriminator_module.apply(variables,
transitions.observation,
transitions.action,
transitions.next_observation,
is_training=is_training,
rng=rng,
mutable=state.keys())
def init_fn(rng):
variables = discriminator_module.init(rng,
dummy_obs,
dummy_actions,
dummy_obs,
is_training=False,
rng=rng)
init_state, discriminator_params = variables.pop('params')
return discriminator_params, init_state
dummy_obs = utils.zeros_like(spec.observations)
dummy_obs = utils.add_batch_dim(dummy_obs)
dummy_actions = utils.zeros_like(spec.actions)
dummy_actions = utils.add_batch_dim(dummy_actions)
discriminator_network = networks_lib.FeedForwardNetwork(init=init_fn,
apply=apply_fn)
networks = ail.AILNetworks(discriminator_network, lambda x: x,
networks)
builder = ail.AILBuilder(
base_builder,
config=ail.AILConfig(is_sequence_based=False,
share_iterator=True,
direct_rl_batch_size=direct_rl_batch_size,
discriminator_batch_size=2,
policy_variable_name=None,
min_replay_size=1),
discriminator_loss=ail.losses.gail_loss(),
make_demonstrations=fakes.transition_iterator(environment))
counter = counting.Counter()
# 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,
counter=counter,
)
# Train the agent.
train_loop = acme.EnvironmentLoop(environment, agent, counter=counter)
train_loop.run(num_episodes=1)
def main(_):
# Create an environment, grab the spec, and use it to create networks.
environment = helpers.make_environment(task=FLAGS.env_name)
environment_spec = specs.make_environment_spec(environment)
agent_networks = ppo.make_continuous_networks(environment_spec)
# Construct the agent.
ppo_config = ppo.PPOConfig(unroll_length=FLAGS.unroll_length,
num_minibatches=FLAGS.ppo_num_minibatches,
num_epochs=FLAGS.ppo_num_epochs,
batch_size=FLAGS.transition_batch_size //
FLAGS.unroll_length,
learning_rate=0.0003,
entropy_cost=0,
gae_lambda=0.8,
value_cost=0.25)
ppo_networks = ppo.make_continuous_networks(environment_spec)
if FLAGS.pretrain:
ppo_networks = add_bc_pretraining(ppo_networks)
discriminator_batch_size = FLAGS.transition_batch_size
ail_config = ail.AILConfig(
direct_rl_batch_size=ppo_config.batch_size * ppo_config.unroll_length,
discriminator_batch_size=discriminator_batch_size,
is_sequence_based=True,
num_sgd_steps_per_step=FLAGS.num_discriminator_steps_per_step,
share_iterator=FLAGS.share_iterator,
)
def discriminator(*args, **kwargs) -> networks_lib.Logits:
# Note: observation embedding is not needed for e.g. Mujoco.
return ail.DiscriminatorModule(
environment_spec=environment_spec,
use_action=True,
use_next_obs=True,
network_core=ail.DiscriminatorMLP([4, 4], ),
)(*args, **kwargs)
discriminator_transformed = hk.without_apply_rng(
hk.transform_with_state(discriminator))
ail_network = ail.AILNetworks(
ail.make_discriminator(environment_spec, discriminator_transformed),
imitation_reward_fn=ail.rewards.gail_reward(),
direct_rl_networks=ppo_networks)
agent = ail.GAIL(spec=environment_spec,
network=ail_network,
config=ail.GAILConfig(ail_config, ppo_config),
seed=FLAGS.seed,
batch_size=ppo_config.batch_size,
make_demonstrations=functools.partial(
helpers.make_demonstration_iterator,
dataset_name=FLAGS.dataset_name),
policy_network=ppo.make_inference_fn(ppo_networks))
# Create the environment loop used for training.
train_logger = experiment_utils.make_experiment_logger(
label='train', steps_key='train_steps')
train_loop = acme.EnvironmentLoop(environment,
agent,
counter=counting.Counter(prefix='train'),
logger=train_logger)
# Create the evaluation actor and loop.
eval_logger = experiment_utils.make_experiment_logger(
label='eval', steps_key='eval_steps')
eval_actor = agent.builder.make_actor(
random_key=jax.random.PRNGKey(FLAGS.seed),
policy_network=ppo.make_inference_fn(agent_networks, evaluation=True),
variable_source=agent)
eval_env = helpers.make_environment(task=FLAGS.env_name)
eval_loop = acme.EnvironmentLoop(eval_env,
eval_actor,
counter=counting.Counter(prefix='eval'),
logger=eval_logger)
assert FLAGS.num_steps % FLAGS.eval_every == 0
for _ in range(FLAGS.num_steps // FLAGS.eval_every):
eval_loop.run(num_episodes=5)
train_loop.run(num_steps=FLAGS.eval_every)
eval_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))