"""Sampler that runs workers in the main process."""
import copy
import psutil
from garage import EpisodeBatch
from garage.experiment.deterministic import get_seed
from garage.sampler.default_worker import DefaultWorker
from garage.sampler.sampler import Sampler
from garage.sampler.worker_factory import WorkerFactory
[docs]class LocalSampler(Sampler):
"""Sampler that runs workers in the main process.
This is probably the simplest possible sampler. It's called the "Local"
sampler because it runs everything in the same process and thread as where
it was called from.
The sampler need to be created either from a worker factory or from
parameters which can construct a worker factory. See the __init__ method
of WorkerFactory for the detail of these parameters.
Args:
agents (Policy or List[Policy]): Agent(s) to use to sample episodes.
If a list is passed in, it must have length exactly
`worker_factory.n_workers`, and will be spread across the
workers.
envs (Environment or List[Environment]): Environment from which
episodes are sampled. If a list is passed in, it must have length
exactly `worker_factory.n_workers`, and will be spread across the
workers.
worker_factory (WorkerFactory): Pickleable factory for creating
workers. Should be transmitted to other processes / nodes where
work needs to be done, then workers should be constructed
there. Either this param or params after this are required to
construct a sampler.
max_episode_length(int): Params used to construct a worker factory.
The maximum length episodes which will be sampled.
is_tf_worker (bool): Whether it is workers for TFTrainer.
seed(int): The seed to use to initialize random number generators.
n_workers(int): The number of workers to use.
worker_class(type): Class of the workers. Instances should implement
the Worker interface.
worker_args (dict or None): Additional arguments that should be passed
to the worker.
"""
def __init__(
self,
agents,
envs,
*, # After this require passing by keyword.
worker_factory=None,
max_episode_length=None,
is_tf_worker=False,
seed=get_seed(),
n_workers=psutil.cpu_count(logical=False),
worker_class=DefaultWorker,
worker_args=None):
# pylint: disable=super-init-not-called
if worker_factory is None and max_episode_length is None:
raise TypeError('Must construct a sampler from WorkerFactory or'
'parameters (at least max_episode_length)')
if isinstance(worker_factory, WorkerFactory):
self._factory = worker_factory
else:
self._factory = WorkerFactory(
max_episode_length=max_episode_length,
is_tf_worker=is_tf_worker,
seed=seed,
n_workers=n_workers,
worker_class=worker_class,
worker_args=worker_args)
self._agents = self._factory.prepare_worker_messages(agents)
self._envs = self._factory.prepare_worker_messages(
envs, preprocess=copy.deepcopy)
self._workers = [
self._factory(i) for i in range(self._factory.n_workers)
]
for worker, agent, env in zip(self._workers, self._agents, self._envs):
worker.update_agent(agent)
worker.update_env(env)
self.total_env_steps = 0
[docs] @classmethod
def from_worker_factory(cls, worker_factory, agents, envs):
"""Construct this sampler.
Args:
worker_factory (WorkerFactory): Pickleable factory for creating
workers. Should be transmitted to other processes / nodes where
work needs to be done, then workers should be constructed
there.
agents (Agent or List[Agent]): Agent(s) to use to sample episodes.
If a list is passed in, it must have length exactly
`worker_factory.n_workers`, and will be spread across the
workers.
envs (Environment or List[Environment]): Environment from which
episodes are sampled. If a list is passed in, it must have
length exactly `worker_factory.n_workers`, and will be spread
across the workers.
Returns:
Sampler: An instance of `cls`.
"""
return cls(agents, envs, worker_factory=worker_factory)
def _update_workers(self, agent_update, env_update):
"""Apply updates to the workers.
Args:
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
"""
agent_updates = self._factory.prepare_worker_messages(agent_update)
env_updates = self._factory.prepare_worker_messages(
env_update, preprocess=copy.deepcopy)
for worker, agent_up, env_up in zip(self._workers, agent_updates,
env_updates):
worker.update_agent(agent_up)
worker.update_env(env_up)
[docs] def obtain_samples(self, itr, num_samples, agent_update, env_update=None):
"""Collect at least a given number transitions (timesteps).
Args:
itr(int): The current iteration number. Using this argument is
deprecated.
num_samples (int): Minimum number of transitions / timesteps to
sample.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: The batch of collected episodes.
"""
self._update_workers(agent_update, env_update)
batches = []
completed_samples = 0
while True:
for worker in self._workers:
batch = worker.rollout()
completed_samples += len(batch.actions)
batches.append(batch)
if completed_samples >= num_samples:
samples = EpisodeBatch.concatenate(*batches)
self.total_env_steps += sum(samples.lengths)
return samples
[docs] def obtain_exact_episodes(self,
n_eps_per_worker,
agent_update,
env_update=None):
"""Sample an exact number of episodes per worker.
Args:
n_eps_per_worker (int): Exact number of episodes to gather for
each worker.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before samplin episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: Batch of gathered episodes. Always in worker
order. In other words, first all episodes from worker 0,
then all episodes from worker 1, etc.
"""
self._update_workers(agent_update, env_update)
batches = []
for worker in self._workers:
for _ in range(n_eps_per_worker):
batch = worker.rollout()
batches.append(batch)
samples = EpisodeBatch.concatenate(*batches)
self.total_env_steps += sum(samples.lengths)
return samples
[docs] def shutdown_worker(self):
"""Shutdown the workers."""
for worker in self._workers:
worker.shutdown()
def __getstate__(self):
"""Get the pickle state.
Returns:
dict: The pickled state.
"""
state = self.__dict__.copy()
# Workers aren't picklable (but WorkerFactory is).
state['_workers'] = None
return state
def __setstate__(self, state):
"""Unpickle the state.
Args:
state (dict): Unpickled state.
"""
self.__dict__.update(state)
self._workers = [
self._factory(i) for i in range(self._factory.n_workers)
]
for worker, agent, env in zip(self._workers, self._agents, self._envs):
worker.update_agent(agent)
worker.update_env(env)