def main(_):
environment_factory = lp_utils.partial_kwargs(helpers.make_environment,
task=FLAGS.task)
program = d4pg.DistributedD4PG(environment_factory=environment_factory,
network_factory=lp_utils.partial_kwargs(
helpers.make_networks),
num_actors=2).build()
lp.launch(program, xm_resources=lp_utils.make_xm_docker_resources(program))
def main(_):
environment_factory = lp_utils.partial_kwargs(helpers.make_environment,
task=FLAGS.task)
program = d4pg.DistributedD4PG(environment_factory=environment_factory,
network_factory=lp_utils.partial_kwargs(
helpers.make_networks),
num_actors=2).build()
lp.launch(program, lp.LaunchType.LOCAL_MULTI_PROCESSING)
def main(_):
# Configure the environment factory with requested task.
make_environment = functools.partial(helpers.make_environment,
domain_name=_DOMAIN.value,
task_name=_TASK.value)
# Construct the program.
program_builder = d4pg.DistributedD4PG(
make_environment,
make_networks,
max_actor_steps=_MAX_ACTOR_STEPS.value,
num_actors=4)
# Launch experiment.
lp.launch(programs=program_builder.build())
def test_control_suite(self):
"""Tests that the agent can run on the control suite without crashing."""
agent = d4pg.DistributedD4PG(
environment_factory=lambda x: fakes.ContinuousEnvironment(bounded=
True),
network_factory=make_networks,
num_actors=2,
batch_size=32,
min_replay_size=32,
max_replay_size=1000,
)
program = agent.build()
(learner_node, ) = program.groups['learner']
learner_node.disable_run()
lp.launch(program, launch_type='test_mt')
learner: acme.Learner = learner_node.create_handle().dereference()
for _ in range(5):
learner.step()