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.
config = ppo.PPOConfig(unroll_length=FLAGS.unroll_length,
num_minibatches=FLAGS.num_minibatches,
num_epochs=FLAGS.num_epochs,
batch_size=FLAGS.batch_size)
learner_logger = experiment_utils.make_experiment_logger(
label='learner', steps_key='learner_steps')
agent = ppo.PPO(environment_spec,
agent_networks,
config=config,
seed=FLAGS.seed,
counter=counting.Counter(prefix='learner'),
logger=learner_logger)
# 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 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 = value_dice.make_networks(environment_spec)
# Construct the agent.
config = value_dice.ValueDiceConfig(
num_sgd_steps_per_step=FLAGS.num_sgd_steps_per_step)
agent = value_dice.ValueDice(environment_spec,
agent_networks,
config=config,
make_demonstrations=functools.partial(
helpers.make_demonstration_iterator,
dataset_name=FLAGS.dataset_name),
seed=FLAGS.seed)
# 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=value_dice.apply_policy_and_sample(agent_networks,
eval_mode=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 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 = td3.make_networks(environment_spec)
# Construct the agent.
config = td3.TD3Config(num_sgd_steps_per_step=FLAGS.num_sgd_steps_per_step)
agent = td3.TD3(environment_spec,
agent_networks,
config=config,
seed=FLAGS.seed)
# 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=td3.get_default_behavior_policy(
agent_networks, 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 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 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)