def main(_):
environment_factory = lp_utils.partial_kwargs(helpers.make_environment,
domain_name=FLAGS.domain,
task_name=FLAGS.task)
batch_size = 32
sequence_length = 20
gradient_steps_per_actor_step = 1.0
samples_per_insert = (gradient_steps_per_actor_step * batch_size *
sequence_length)
num_actors = 1
program = svg0_prior.DistributedSVG0(
environment_factory=environment_factory,
network_factory=lp_utils.partial_kwargs(
svg0_prior.make_default_networks),
batch_size=batch_size,
sequence_length=sequence_length,
samples_per_insert=samples_per_insert,
entropy_regularizer_cost=1e-4,
max_replay_size=int(2e6),
target_update_period=250,
num_actors=num_actors).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, 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 test_agent(self, distributional_critic):
# Create objectives.
reward_objectives, qvalue_objectives = make_objectives()
network_factory = lp_utils.partial_kwargs(
make_networks, distributional_critic=distributional_critic)
agent = mompo.DistributedMultiObjectiveMPO(
reward_objectives,
qvalue_objectives,
environment_factory=make_environment,
network_factory=network_factory,
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()
def test_partial_kwargs(self):
def foo(a, b, c=2):
return a, b, c
def bar(a, b):
return a, b
# Override the default values. The last two should be no-ops.
foo1 = lp_utils.partial_kwargs(foo, c=1)
foo2 = lp_utils.partial_kwargs(foo)
bar1 = lp_utils.partial_kwargs(bar)
# Check that we raise errors on overriding kwargs with no default values
with self.assertRaises(ValueError):
lp_utils.partial_kwargs(foo, a=2)
# CHeck the we raise if we try to override a kwarg that doesn't exist.
with self.assertRaises(ValueError):
lp_utils.partial_kwargs(foo, d=2)
# Make sure we get back the correct values.
self.assertEqual(foo1(1, 2), (1, 2, 1))
self.assertEqual(foo2(1, 2), (1, 2, 2))
self.assertEqual(bar1(1, 2), (1, 2))