def learner(
self,
random_key: networks_lib.PRNGKey,
counter: counting.Counter,
):
"""The Learning part of the agent."""
# Counter and logger.
counter = counting.Counter(counter, 'learner')
logger = loggers.make_default_logger(
'learner',
self._save_logs,
time_delta=self._log_every,
asynchronous=True,
serialize_fn=utils.fetch_devicearray,
steps_key='learner_steps')
# Create the learner.
networks = self._network_factory()
learner = self._make_learner(random_key, networks, counter, logger)
kwargs = {
'directory': self._workdir,
'add_uid': self._workdir == '~/acme'
}
# Return the learning agent.
return savers.CheckpointingRunner(learner,
subdirectory='learner',
time_delta_minutes=5,
**kwargs)
def counter(self):
kwargs = {
'directory': self._workdir,
'add_uid': self._workdir == '~/acme'
}
return savers.CheckpointingRunner(counting.Counter(),
subdirectory='counter',
time_delta_minutes=5,
**kwargs)
def counter(self):
return savers.CheckpointingRunner(
counting.Counter(),
key='counter',
subdirectory='counter',
time_delta_minutes=5,
directory=self._checkpointing_config.directory,
add_uid=self._checkpointing_config.add_uid,
max_to_keep=self._checkpointing_config.max_to_keep)
def counter(self):
kwargs = {}
if self._checkpointing_config:
kwargs = vars(self._checkpointing_config)
return savers.CheckpointingRunner(counting.Counter(),
key='counter',
subdirectory='counter',
time_delta_minutes=5,
**kwargs)
def learner(
self,
random_key: networks_lib.PRNGKey,
replay: reverb.Client,
counter: counting.Counter,
):
"""The Learning part of the agent."""
iterator = self._builder.make_dataset_iterator(replay)
dummy_seed = 1
environment_spec = (self._environment_spec
or specs.make_environment_spec(
self._environment_factory(dummy_seed)))
# Creates the networks to optimize (online) and target networks.
networks = self._network_factory(environment_spec)
if self._prefetch_size > 1:
# When working with single GPU we should prefetch to device for
# efficiency. If running on TPU this isn't necessary as the computation
# and input placement can be done automatically. For multi-gpu currently
# the best solution is to pre-fetch to host although this may change in
# the future.
device = jax.devices()[0] if self._device_prefetch else None
iterator = utils.prefetch(iterator,
buffer_size=self._prefetch_size,
device=device)
else:
logging.info('Not prefetching the iterator.')
counter = counting.Counter(counter, 'learner')
learner = self._builder.make_learner(random_key, networks, iterator,
replay, counter)
return savers.CheckpointingRunner(
learner,
key='learner',
subdirectory='learner',
time_delta_minutes=5,
directory=self._checkpointing_config.directory,
add_uid=self._checkpointing_config.add_uid,
max_to_keep=self._checkpointing_config.max_to_keep)
def build_learner(
random_key: networks_lib.PRNGKey,
replay: reverb.Client,
counter: Optional[counting.Counter] = None,
primary_learner: Optional[core.Learner] = None,
):
"""The Learning part of the agent."""
dummy_seed = 1
spec = (experiment.environment_spec or specs.make_environment_spec(
experiment.environment_factory(dummy_seed)))
# Creates the networks to optimize (online) and target networks.
networks = experiment.network_factory(spec)
iterator = experiment.builder.make_dataset_iterator(replay)
# make_dataset_iterator is responsible for putting data onto appropriate
# training devices, so here we apply prefetch, so that data is copied over
# in the background.
iterator = utils.prefetch(iterable=iterator, buffer_size=1)
counter = counting.Counter(counter, 'learner')
learner = experiment.builder.make_learner(random_key, networks,
iterator,
experiment.logger_factory,
spec, replay, counter)
if primary_learner is None:
learner = savers.CheckpointingRunner(
learner,
key='learner',
subdirectory='learner',
time_delta_minutes=5,
directory=checkpointing_config.directory,
add_uid=checkpointing_config.add_uid,
max_to_keep=checkpointing_config.max_to_keep)
else:
learner.restore(primary_learner.save())
# NOTE: This initially synchronizes secondary learner states with the
# primary one. Further synchronization should be handled by the learner
# properly doing a pmap/pmean on the loss/gradients, respectively.
return learner
def build_learner(
random_key: networks_lib.PRNGKey,
counter: Optional[counting.Counter] = None,
):
"""The Learning part of the agent."""
dummy_seed = 1
spec = (experiment.environment_spec or specs.make_environment_spec(
experiment.environment_factory(dummy_seed)))
# Creates the networks to optimize (online) and target networks.
networks = experiment.network_factory(spec)
dataset_key, random_key = jax.random.split(random_key)
iterator = experiment.demonstration_dataset_factory(dataset_key)
# make_demonstrations is responsible for putting data onto appropriate
# training devices, so here we apply prefetch, so that data is copied over
# in the background.
iterator = utils.prefetch(iterable=iterator, buffer_size=1)
counter = counting.Counter(counter, 'learner')
learner = experiment.builder.make_learner(
random_key=random_key,
networks=networks,
dataset=iterator,
logger_fn=experiment.logger_factory,
environment_spec=spec,
counter=counter)
learner = savers.CheckpointingRunner(
learner,
key='learner',
subdirectory='learner',
time_delta_minutes=5,
directory=checkpointing_config.directory,
add_uid=checkpointing_config.add_uid,
max_to_keep=checkpointing_config.max_to_keep)
return learner
def learner(
self,
random_key,
replay,
counter,
):
"""The Learning part of the agent."""
if self._builder._config.env_name.startswith('offline_ant'): # pytype: disable=attribute-error, pylint: disable=protected-access
adder = self._builder.make_adder(replay)
env = self._environment_factory(0)
dataset = env.get_dataset() # pytype: disable=attribute-error
for t in tqdm.trange(dataset['observations'].shape[0]):
discount = 1.0
if t == 0 or dataset['timeouts'][t - 1]:
step_type = dm_env.StepType.FIRST
elif dataset['timeouts'][t]:
step_type = dm_env.StepType.LAST
discount = 0.0
else:
step_type = dm_env.StepType.MID
ts = dm_env.TimeStep(
step_type=step_type,
reward=dataset['rewards'][t],
discount=discount,
observation=np.concatenate([dataset['observations'][t],
dataset['infos/goal'][t]]),
)
if t == 0 or dataset['timeouts'][t - 1]:
adder.add_first(ts) # pytype: disable=attribute-error
else:
adder.add(action=dataset['actions'][t-1], next_timestep=ts) # pytype: disable=attribute-error
if self._builder._config.local and t > 10_000: # pytype: disable=attribute-error, pylint: disable=protected-access
break
iterator = self._builder.make_dataset_iterator(replay)
dummy_seed = 1
environment_spec = (
self._environment_spec or
specs.make_environment_spec(self._environment_factory(dummy_seed)))
# Creates the networks to optimize (online) and target networks.
networks = self._network_factory(environment_spec)
if self._prefetch_size > 1:
# When working with single GPU we should prefetch to device for
# efficiency. If running on TPU this isn't necessary as the computation
# and input placement can be done automatically. For multi-gpu currently
# the best solution is to pre-fetch to host although this may change in
# the future.
device = jax.devices()[0] if self._device_prefetch else None
iterator = utils.prefetch(
iterator, buffer_size=self._prefetch_size, device=device)
else:
logging.info('Not prefetching the iterator.')
counter = counting.Counter(counter, 'learner')
learner = self._builder.make_learner(random_key, networks, iterator, replay,
counter)
kwargs = {}
if self._checkpointing_config:
kwargs = vars(self._checkpointing_config)
# Return the learning agent.
return savers.CheckpointingRunner(
learner,
key='learner',
subdirectory='learner',
time_delta_minutes=5,
**kwargs)