import functools
import gymnasium as gym
import logging
import importlib.util
import os
from typing import (
Any,
Callable,
Collection,
Dict,
List,
Optional,
Tuple,
Type,
TYPE_CHECKING,
TypeVar,
Union,
)
import ray
from ray.actor import ActorHandle
from ray.exceptions import RayActorError
from ray.rllib.core import (
COMPONENT_ENV_TO_MODULE_CONNECTOR,
COMPONENT_LEARNER,
COMPONENT_MODULE_TO_ENV_CONNECTOR,
COMPONENT_RL_MODULE,
)
from ray.rllib.core.learner import LearnerGroup
from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
from ray.rllib.evaluation.rollout_worker import RolloutWorker
from ray.rllib.utils.actor_manager import RemoteCallResults
from ray.rllib.env.base_env import BaseEnv
from ray.rllib.env.env_context import EnvContext
from ray.rllib.env.env_runner import EnvRunner
from ray.rllib.offline import get_dataset_and_shards
from ray.rllib.policy.policy import Policy, PolicyState
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.utils.actor_manager import FaultTolerantActorManager
from ray.rllib.utils.deprecation import (
Deprecated,
deprecation_warning,
DEPRECATED_VALUE,
)
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.metrics import NUM_ENV_STEPS_SAMPLED_LIFETIME
from ray.rllib.utils.policy import validate_policy_id
from ray.rllib.utils.typing import (
AgentID,
EnvCreator,
EnvType,
EpisodeID,
PartialAlgorithmConfigDict,
PolicyID,
SampleBatchType,
TensorType,
)
from ray.util.annotations import DeveloperAPI
if TYPE_CHECKING:
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
tf1, tf, tfv = try_import_tf()
logger = logging.getLogger(__name__)
# Generic type var for foreach_* methods.
T = TypeVar("T")
[docs]@DeveloperAPI
class EnvRunnerGroup:
"""Set of EnvRunners with n @ray.remote workers and zero or one local worker.
Where: n >= 0.
"""
[docs] def __init__(
self,
*,
env_creator: Optional[EnvCreator] = None,
validate_env: Optional[Callable[[EnvType], None]] = None,
default_policy_class: Optional[Type[Policy]] = None,
config: Optional["AlgorithmConfig"] = None,
num_env_runners: int = 0,
local_env_runner: bool = True,
logdir: Optional[str] = None,
_setup: bool = True,
tune_trial_id: Optional[str] = None,
# Deprecated args.
num_workers=DEPRECATED_VALUE,
local_worker=DEPRECATED_VALUE,
):
"""Initializes a EnvRunnerGroup instance.
Args:
env_creator: Function that returns env given env config.
validate_env: Optional callable to validate the generated
environment (only on worker=0). This callable should raise
an exception if the environment is invalid.
default_policy_class: An optional default Policy class to use inside
the (multi-agent) `policies` dict. In case the PolicySpecs in there
have no class defined, use this `default_policy_class`.
If None, PolicySpecs will be using the Algorithm's default Policy
class.
config: Optional AlgorithmConfig (or config dict).
num_env_runners: Number of remote EnvRunners to create.
local_env_runner: Whether to create a local (non @ray.remote) EnvRunner
in the returned set as well (default: True). If `num_env_runners`
is 0, always create a local EnvRunner.
logdir: Optional logging directory for workers.
_setup: Whether to actually set up workers. This is only for testing.
"""
if num_workers != DEPRECATED_VALUE or local_worker != DEPRECATED_VALUE:
deprecation_warning(
old="WorkerSet(num_workers=... OR local_worker=...)",
new="EnvRunnerGroup(num_env_runners=... AND local_env_runner=...)",
error=False,
)
if num_workers != DEPRECATED_VALUE:
num_env_runners = num_workers
if local_worker != DEPRECATED_VALUE:
local_env_runner = local_worker
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
# Make sure `config` is an AlgorithmConfig object.
if not config:
config = AlgorithmConfig()
elif isinstance(config, dict):
config = AlgorithmConfig.from_dict(config)
self._env_creator = env_creator
self._policy_class = default_policy_class
self._remote_config = config
self._remote_args = {
"num_cpus": self._remote_config.num_cpus_per_env_runner,
"num_gpus": self._remote_config.num_gpus_per_env_runner,
"resources": self._remote_config.custom_resources_per_env_runner,
"max_restarts": config.max_num_env_runner_restarts,
}
self._tune_trial_id = tune_trial_id
# Set the EnvRunner subclass to be used as "workers". Default: RolloutWorker.
self.env_runner_cls = config.env_runner_cls
if self.env_runner_cls is None:
if config.enable_env_runner_and_connector_v2:
if config.is_multi_agent():
from ray.rllib.env.multi_agent_env_runner import MultiAgentEnvRunner
self.env_runner_cls = MultiAgentEnvRunner
else:
from ray.rllib.env.single_agent_env_runner import (
SingleAgentEnvRunner,
)
self.env_runner_cls = SingleAgentEnvRunner
else:
self.env_runner_cls = RolloutWorker
self._cls = ray.remote(**self._remote_args)(self.env_runner_cls).remote
self._logdir = logdir
self._ignore_env_runner_failures = config.ignore_env_runner_failures
# Create remote worker manager.
# Note(jungong) : ID 0 is used by the local worker.
# Starting remote workers from ID 1 to avoid conflicts.
self._worker_manager = FaultTolerantActorManager(
max_remote_requests_in_flight_per_actor=(
config["max_requests_in_flight_per_sampler_worker"]
),
init_id=1,
)
if _setup:
try:
self._setup(
validate_env=validate_env,
config=config,
num_env_runners=num_env_runners,
local_env_runner=local_env_runner,
)
# EnvRunnerGroup creation possibly fails, if some (remote) workers cannot
# be initialized properly (due to some errors in the EnvRunners's
# constructor).
except RayActorError as e:
# In case of an actor (remote worker) init failure, the remote worker
# may still exist and will be accessible, however, e.g. calling
# its `sample.remote()` would result in strange "property not found"
# errors.
if e.actor_init_failed:
# Raise the original error here that the EnvRunners raised
# during its construction process. This is to enforce transparency
# for the user (better to understand the real reason behind the
# failure).
# - e.args[0]: The RayTaskError (inside the caught RayActorError).
# - e.args[0].args[2]: The original Exception (e.g. a ValueError due
# to a config mismatch) thrown inside the actor.
raise e.args[0].args[2]
# In any other case, raise the RayActorError as-is.
else:
raise e
def _setup(
self,
*,
validate_env: Optional[Callable[[EnvType], None]] = None,
config: Optional["AlgorithmConfig"] = None,
num_env_runners: int = 0,
local_env_runner: bool = True,
):
"""Sets up an EnvRunnerGroup instance.
Args:
validate_env: Optional callable to validate the generated
environment (only on worker=0).
config: Optional dict that extends the common config of
the Algorithm class.
num_env_runners: Number of remote EnvRunner workers to create.
local_env_runner: Whether to create a local (non @ray.remote) EnvRunner
in the returned set as well (default: True). If `num_env_runners`
is 0, always create a local EnvRunner.
"""
# Force a local worker if num_env_runners == 0 (no remote workers).
# Otherwise, this EnvRunnerGroup would be empty.
self._local_worker = None
if num_env_runners == 0:
local_env_runner = True
# Create a local (learner) version of the config for the local worker.
# The only difference is the tf_session_args, which - for the local worker -
# will be `config.tf_session_args` updated/overridden with
# `config.local_tf_session_args`.
local_tf_session_args = config.tf_session_args.copy()
local_tf_session_args.update(config.local_tf_session_args)
self._local_config = config.copy(copy_frozen=False).framework(
tf_session_args=local_tf_session_args
)
if config.input_ == "dataset":
# Create the set of dataset readers to be shared by all the
# rollout workers.
self._ds, self._ds_shards = get_dataset_and_shards(config, num_env_runners)
else:
self._ds = None
self._ds_shards = None
# Create a number of @ray.remote workers.
self.add_workers(
num_env_runners,
validate=config.validate_env_runners_after_construction,
)
# If num_env_runners > 0 and we don't have an env on the local worker,
# get the observation- and action spaces for each policy from
# the first remote worker (which does have an env).
if (
local_env_runner
and self._worker_manager.num_actors() > 0
and not config.enable_env_runner_and_connector_v2
and not config.create_env_on_local_worker
and (not config.observation_space or not config.action_space)
):
spaces = self._get_spaces_from_remote_worker()
else:
spaces = None
# Create a local worker, if needed.
if local_env_runner:
self._local_worker = self._make_worker(
cls=self.env_runner_cls,
env_creator=self._env_creator,
validate_env=validate_env,
worker_index=0,
num_workers=num_env_runners,
config=self._local_config,
spaces=spaces,
)
def _get_spaces_from_remote_worker(self):
"""Infer observation and action spaces from a remote worker.
Returns:
A dict mapping from policy ids to spaces.
"""
# Get ID of the first remote worker.
worker_id = self._worker_manager.actor_ids()[0]
# Try to figure out spaces from the first remote worker.
# Traditional RolloutWorker.
if issubclass(self.env_runner_cls, RolloutWorker):
remote_spaces = self.foreach_worker(
lambda worker: worker.foreach_policy(
lambda p, pid: (pid, p.observation_space, p.action_space)
),
remote_worker_ids=[worker_id],
local_worker=False,
)
# Generic EnvRunner.
else:
remote_spaces = self.foreach_worker(
lambda worker: worker.marl_module.foreach_module(
lambda mid, m: (
mid,
m.config.observation_space,
m.config.action_space,
),
)
if hasattr(worker, "marl_module")
else [
(
DEFAULT_POLICY_ID,
worker.module.config.observation_space,
worker.module.config.action_space,
),
]
)
if not remote_spaces:
raise ValueError(
"Could not get observation and action spaces from remote "
"worker. Maybe specify them manually in the config?"
)
spaces = {
e[0]: (getattr(e[1], "original_space", e[1]), e[2])
for e in remote_spaces[0]
}
if issubclass(self.env_runner_cls, RolloutWorker):
# Try to add the actual env's obs/action spaces.
env_spaces = self.foreach_worker(
lambda worker: worker.foreach_env(
lambda env: (env.observation_space, env.action_space)
),
remote_worker_ids=[worker_id],
local_worker=False,
)
if env_spaces:
# env_spaces group spaces by environment then worker.
# So need to unpack thing twice.
spaces["__env__"] = env_spaces[0][0]
logger.info(
"Inferred observation/action spaces from remote "
f"worker (local worker has no env): {spaces}"
)
return spaces
@property
def local_env_runner(self) -> EnvRunner:
"""Returns the local EnvRunner."""
return self._local_worker
[docs] @DeveloperAPI
def local_worker(self) -> EnvRunner:
"""Returns the local EnvRunner."""
return self._local_worker
[docs] @DeveloperAPI
def healthy_worker_ids(self) -> List[int]:
"""Returns the list of remote worker IDs."""
return self._worker_manager.healthy_actor_ids()
[docs] @DeveloperAPI
def num_remote_env_runners(self) -> int:
"""Returns the number of remote EnvRunners."""
return self._worker_manager.num_actors()
[docs] @DeveloperAPI
def num_remote_workers(self) -> int:
"""Returns the number of remote EnvRunners."""
return self._worker_manager.num_actors()
[docs] @DeveloperAPI
def num_healthy_remote_workers(self) -> int:
"""Returns the number of healthy remote workers."""
return self._worker_manager.num_healthy_actors()
[docs] @DeveloperAPI
def num_healthy_workers(self) -> int:
"""Returns the number of all healthy workers, including the local worker."""
return int(bool(self._local_worker)) + self.num_healthy_remote_workers()
[docs] @DeveloperAPI
def num_in_flight_async_reqs(self) -> int:
"""Returns the number of in-flight async requests."""
return self._worker_manager.num_outstanding_async_reqs()
[docs] @DeveloperAPI
def num_remote_worker_restarts(self) -> int:
"""Total number of times managed remote workers have been restarted."""
return self._worker_manager.total_num_restarts()
[docs] @DeveloperAPI
def sync_env_runner_states(
self,
*,
config: "AlgorithmConfig",
from_worker: Optional[EnvRunner] = None,
env_steps_sampled: Optional[int] = None,
connector_states: Optional[List[Dict[str, Any]]] = None,
rl_module_state: Optional[Dict[str, Any]] = None,
env_runner_indices_to_update: Optional[List[int]] = None,
) -> None:
"""Synchronizes the connectors of this EnvRunnerGroup's EnvRunners.
The exact procedure works as follows:
- If `from_worker` is None, set `from_worker=self.local_worker()`.
- If `config.use_worker_filter_stats` is True, gather all remote EnvRunners'
ConnectorV2 states. Otherwise, only use the ConnectorV2 states of `from_worker`.
- Merge all gathered states into one resulting state.
- Broadcast the resulting state back to all remote EnvRunners AND the local
EnvRunner.
Args:
config: The AlgorithmConfig object to use to determine, in which
direction(s) we need to synch and what the timeouts are.
from_worker: The EnvRunner from which to synch. If None, will use the local
worker of this EnvRunnerGroup.
env_steps_sampled: The total number of env steps taken thus far by all
workers combined. Used to broadcast this number to all remote workers
if `update_worker_filter_stats` is True in `config`.
env_runner_indices_to_update: The indices of those EnvRunners to update
with the merged state. Use None (default) to update all remote
EnvRunners.
"""
local_worker = self.local_worker()
from_worker = from_worker or local_worker
# Early out if the number of (healthy) remote workers is 0. In this case, the
# local worker is the only operating worker and thus of course always holds
# the reference connector state.
if self.num_healthy_remote_workers() == 0:
self.local_worker().set_state(
{
**(
{NUM_ENV_STEPS_SAMPLED_LIFETIME: env_steps_sampled}
if env_steps_sampled is not None
else {}
),
**(
{COMPONENT_RL_MODULE: rl_module_state}
if rl_module_state is not None
else {}
),
}
)
return
# Also early out, if we a) don't use the remote states AND b) don't want to
# broadcast back from `from_worker` to all remote workers.
# TODO (sven): Rename these to proper "..env_runner_states.." containing names.
if not config.update_worker_filter_stats and not config.use_worker_filter_stats:
return
# Use states from all remote EnvRunners.
if config.use_worker_filter_stats:
if connector_states == []:
env_runner_states = {}
else:
if connector_states is None:
connector_states = self.foreach_worker(
lambda w: w.get_state(
components=[
COMPONENT_ENV_TO_MODULE_CONNECTOR,
COMPONENT_MODULE_TO_ENV_CONNECTOR,
]
),
local_worker=False,
timeout_seconds=(
config.sync_filters_on_rollout_workers_timeout_s
),
)
env_to_module_states = [
s[COMPONENT_ENV_TO_MODULE_CONNECTOR] for s in connector_states
]
module_to_env_states = [
s[COMPONENT_MODULE_TO_ENV_CONNECTOR] for s in connector_states
]
env_runner_states = {
COMPONENT_ENV_TO_MODULE_CONNECTOR: (
local_worker._env_to_module.merge_states(env_to_module_states)
),
COMPONENT_MODULE_TO_ENV_CONNECTOR: (
local_worker._module_to_env.merge_states(module_to_env_states)
),
}
# Ignore states from remote EnvRunners (use the current `from_worker` states
# only).
else:
env_runner_states = from_worker.get_state(
components=[
COMPONENT_ENV_TO_MODULE_CONNECTOR,
COMPONENT_MODULE_TO_ENV_CONNECTOR,
]
)
# Update the global number of environment steps, if necessary.
if env_steps_sampled is not None:
env_runner_states[NUM_ENV_STEPS_SAMPLED_LIFETIME] = env_steps_sampled
# Update the rl_module component of the EnvRunner states, if necessary:
if rl_module_state:
env_runner_states[COMPONENT_RL_MODULE] = rl_module_state
# If we do NOT want remote EnvRunners to get their Connector states updated,
# only update the local worker here (with all state components) and then remove
# the connector components.
if not config.update_worker_filter_stats:
local_worker.set_state(env_runner_states)
del env_runner_states[COMPONENT_ENV_TO_MODULE_CONNECTOR]
del env_runner_states[COMPONENT_MODULE_TO_ENV_CONNECTOR]
# If there are components in the state left -> Update remote workers with these
# state components (and maybe the local worker, if it hasn't been updated yet).
if env_runner_states:
# Put the state dictionary into Ray's object store to avoid having to make n
# pickled copies of the state dict.
ref_env_runner_states = ray.put(env_runner_states)
def _update(_env_runner: EnvRunner) -> None:
_env_runner.set_state(ray.get(ref_env_runner_states))
# Broadcast updated states back to all workers.
self.foreach_worker(
_update,
remote_worker_ids=env_runner_indices_to_update,
local_worker=config.update_worker_filter_stats,
timeout_seconds=0.0, # This is a state update -> Fire-and-forget.
)
[docs] @DeveloperAPI
def sync_weights(
self,
policies: Optional[List[PolicyID]] = None,
from_worker_or_learner_group: Optional[Union[EnvRunner, "LearnerGroup"]] = None,
to_worker_indices: Optional[List[int]] = None,
global_vars: Optional[Dict[str, TensorType]] = None,
timeout_seconds: Optional[float] = 0.0,
inference_only: Optional[bool] = False,
) -> None:
"""Syncs model weights from the given weight source to all remote workers.
Weight source can be either a (local) rollout worker or a learner_group. It
should just implement a `get_weights` method.
Args:
policies: Optional list of PolicyIDs to sync weights for.
If None (default), sync weights to/from all policies.
from_worker_or_learner_group: Optional (local) EnvRunner instance or
LearnerGroup instance to sync from. If None (default),
sync from this EnvRunnerGroup's local worker.
to_worker_indices: Optional list of worker indices to sync the
weights to. If None (default), sync to all remote workers.
global_vars: An optional global vars dict to set this
worker to. If None, do not update the global_vars.
timeout_seconds: Timeout in seconds to wait for the sync weights
calls to complete. Default is 0.0 (fire-and-forget, do not wait
for any sync calls to finish). Setting this to 0.0 might significantly
improve algorithm performance, depending on the algo's `training_step`
logic.
inference_only: Sync weights with workers that keep inference-only
modules. This is needed for algorithms in the new stack that
use inference-only modules. In this case only a part of the
parameters are synced to the workers. Default is False.
"""
if self.local_worker() is None and from_worker_or_learner_group is None:
raise TypeError(
"No `local_worker` in EnvRunnerGroup, must provide "
"`from_worker_or_learner_group` arg in `sync_weights()`!"
)
# Only sync if we have remote workers or `from_worker_or_trainer` is provided.
rl_module_state = None
if self.num_remote_workers() or from_worker_or_learner_group is not None:
weights_src = from_worker_or_learner_group or self.local_worker()
if weights_src is None:
raise ValueError(
"`from_worker_or_trainer` is None. In this case, EnvRunnerGroup "
"should have local_env_runner. But local_env_runner is also None."
)
modules = (
[COMPONENT_RL_MODULE + "/" + p for p in policies]
if policies is not None
else [COMPONENT_RL_MODULE]
)
# LearnerGroup has-a Learner has-a RLModule.
if isinstance(weights_src, LearnerGroup):
rl_module_state = weights_src.get_state(
components=[COMPONENT_LEARNER + "/" + m for m in modules],
inference_only=inference_only,
)[COMPONENT_LEARNER][COMPONENT_RL_MODULE]
# EnvRunner has-a RLModule.
elif self._remote_config.enable_env_runner_and_connector_v2:
rl_module_state = weights_src.get_state(
components=modules,
inference_only=inference_only,
)[COMPONENT_RL_MODULE]
else:
rl_module_state = weights_src.get_weights(
policies=policies,
inference_only=inference_only,
)
# Move weights to the object store to avoid having to make n pickled copies
# of the weights dict for each worker.
rl_module_state_ref = ray.put(rl_module_state)
if self._remote_config.enable_env_runner_and_connector_v2:
def _set_weights(env_runner):
_rl_module_state = ray.get(rl_module_state_ref)
env_runner.set_state({COMPONENT_RL_MODULE: _rl_module_state})
else:
def _set_weights(env_runner):
_weights = ray.get(rl_module_state_ref)
env_runner.set_weights(_weights, global_vars)
# Sync to specified remote workers in this EnvRunnerGroup.
self.foreach_worker(
func=_set_weights,
local_worker=False, # Do not sync back to local worker.
remote_worker_ids=to_worker_indices,
timeout_seconds=timeout_seconds,
)
# If `from_worker_or_learner_group` is provided, also sync to this
# EnvRunnerGroup's local worker.
if self.local_worker() is not None:
if from_worker_or_learner_group is not None:
if self._remote_config.enable_env_runner_and_connector_v2:
self.local_worker().set_state(
{COMPONENT_RL_MODULE: rl_module_state}
)
else:
self.local_worker().set_weights(rl_module_state)
# If `global_vars` is provided and local worker exists -> Update its
# global_vars.
if global_vars is not None:
self.local_worker().set_global_vars(global_vars)
[docs] @DeveloperAPI
def add_policy(
self,
policy_id: PolicyID,
policy_cls: Optional[Type[Policy]] = None,
policy: Optional[Policy] = None,
*,
observation_space: Optional[gym.spaces.Space] = None,
action_space: Optional[gym.spaces.Space] = None,
config: Optional[Union["AlgorithmConfig", PartialAlgorithmConfigDict]] = None,
policy_state: Optional[PolicyState] = None,
policy_mapping_fn: Optional[Callable[[AgentID, EpisodeID], PolicyID]] = None,
policies_to_train: Optional[
Union[
Collection[PolicyID],
Callable[[PolicyID, Optional[SampleBatchType]], bool],
]
] = None,
module_spec: Optional[SingleAgentRLModuleSpec] = None,
# Deprecated.
workers: Optional[List[Union[EnvRunner, ActorHandle]]] = DEPRECATED_VALUE,
) -> None:
"""Adds a policy to this EnvRunnerGroup's workers or a specific list of workers.
Args:
policy_id: ID of the policy to add.
policy_cls: The Policy class to use for constructing the new Policy.
Note: Only one of `policy_cls` or `policy` must be provided.
policy: The Policy instance to add to this EnvRunnerGroup. If not None, the
given Policy object will be directly inserted into the
local worker and clones of that Policy will be created on all remote
workers.
Note: Only one of `policy_cls` or `policy` must be provided.
observation_space: The observation space of the policy to add.
If None, try to infer this space from the environment.
action_space: The action space of the policy to add.
If None, try to infer this space from the environment.
config: The config object or overrides for the policy to add.
policy_state: Optional state dict to apply to the new
policy instance, right after its construction.
policy_mapping_fn: An optional (updated) policy mapping function
to use from here on. Note that already ongoing episodes will
not change their mapping but will use the old mapping till
the end of the episode.
policies_to_train: An optional list of policy IDs to be trained
or a callable taking PolicyID and SampleBatchType and
returning a bool (trainable or not?).
If None, will keep the existing setup in place. Policies,
whose IDs are not in the list (or for which the callable
returns False) will not be updated.
module_spec: In the new RLModule API we need to pass in the module_spec for
the new module that is supposed to be added. Knowing the policy spec is
not sufficient.
workers: A list of EnvRunner/ActorHandles (remote
EnvRunners) to add this policy to. If defined, will only
add the given policy to these workers.
Raises:
KeyError: If the given `policy_id` already exists in this EnvRunnerGroup.
"""
if self.local_worker() and policy_id in self.local_worker().policy_map:
raise KeyError(
f"Policy ID '{policy_id}' already exists in policy map! "
"Make sure you use a Policy ID that has not been taken yet."
" Policy IDs that are already in your policy map: "
f"{list(self.local_worker().policy_map.keys())}"
)
if workers is not DEPRECATED_VALUE:
deprecation_warning(
old="EnvRunnerGroup.add_policy(.., workers=..)",
help=(
"The `workers` argument to `EnvRunnerGroup.add_policy()` is "
"deprecated! Please do not use it anymore."
),
error=True,
)
if (policy_cls is None) == (policy is None):
raise ValueError(
"Only one of `policy_cls` or `policy` must be provided to "
"staticmethod: `EnvRunnerGroup.add_policy()`!"
)
validate_policy_id(policy_id, error=False)
# Policy instance not provided: Use the information given here.
if policy_cls is not None:
new_policy_instance_kwargs = dict(
policy_id=policy_id,
policy_cls=policy_cls,
observation_space=observation_space,
action_space=action_space,
config=config,
policy_state=policy_state,
policy_mapping_fn=policy_mapping_fn,
policies_to_train=list(policies_to_train)
if policies_to_train
else None,
module_spec=module_spec,
)
# Policy instance provided: Create clones of this very policy on the different
# workers (copy all its properties here for the calls to add_policy on the
# remote workers).
else:
new_policy_instance_kwargs = dict(
policy_id=policy_id,
policy_cls=type(policy),
observation_space=policy.observation_space,
action_space=policy.action_space,
config=policy.config,
policy_state=policy.get_state(),
policy_mapping_fn=policy_mapping_fn,
policies_to_train=list(policies_to_train)
if policies_to_train
else None,
module_spec=module_spec,
)
def _create_new_policy_fn(worker):
# `foreach_worker` function: Adds the policy the the worker (and
# maybe changes its policy_mapping_fn - if provided here).
worker.add_policy(**new_policy_instance_kwargs)
if self.local_worker() is not None:
# Add policy directly by (already instantiated) object.
if policy is not None:
self.local_worker().add_policy(
policy_id=policy_id,
policy=policy,
policy_mapping_fn=policy_mapping_fn,
policies_to_train=policies_to_train,
module_spec=module_spec,
)
# Add policy by constructor kwargs.
else:
self.local_worker().add_policy(**new_policy_instance_kwargs)
# Add the policy to all remote workers.
self.foreach_worker(_create_new_policy_fn, local_worker=False)
[docs] @DeveloperAPI
def add_workers(self, num_workers: int, validate: bool = False) -> None:
"""Creates and adds a number of remote workers to this worker set.
Can be called several times on the same EnvRunnerGroup to add more
EnvRunners to the set.
Args:
num_workers: The number of remote Workers to add to this
EnvRunnerGroup.
validate: Whether to validate remote workers after their construction
process.
Raises:
RayError: If any of the constructed remote workers is not up and running
properly.
"""
old_num_workers = self._worker_manager.num_actors()
new_workers = [
self._make_worker(
cls=self._cls,
env_creator=self._env_creator,
validate_env=None,
worker_index=old_num_workers + i + 1,
num_workers=old_num_workers + num_workers,
config=self._remote_config,
)
for i in range(num_workers)
]
self._worker_manager.add_actors(new_workers)
# Validate here, whether all remote workers have been constructed properly
# and are "up and running". Establish initial states.
if validate:
for result in self._worker_manager.foreach_actor(
lambda w: w.assert_healthy()
):
# Simiply raise the error, which will get handled by the try-except
# clause around the _setup().
if not result.ok:
raise result.get()
[docs] @DeveloperAPI
def reset(self, new_remote_workers: List[ActorHandle]) -> None:
"""Hard overrides the remote workers in this set with the given one.
Args:
new_remote_workers: A list of new EnvRunners
(as `ActorHandles`) to use as remote workers.
"""
self._worker_manager.clear()
self._worker_manager.add_actors(new_remote_workers)
[docs] @DeveloperAPI
def stop(self) -> None:
"""Calls `stop` on all rollout workers (including the local one)."""
try:
# Make sure we stop all workers, include the ones that were just
# restarted / recovered or that are tagged unhealthy (at least, we should
# try).
self.foreach_worker(
lambda w: w.stop(), healthy_only=False, local_worker=True
)
except Exception:
logger.exception("Failed to stop workers!")
finally:
self._worker_manager.clear()
[docs] @DeveloperAPI
def is_policy_to_train(
self, policy_id: PolicyID, batch: Optional[SampleBatchType] = None
) -> bool:
"""Whether given PolicyID (optionally inside some batch) is trainable."""
local_worker = self.local_worker()
if local_worker:
if local_worker.is_policy_to_train is None:
return True
return local_worker.is_policy_to_train(policy_id, batch)
else:
raise NotImplementedError
[docs] @DeveloperAPI
def foreach_worker(
self,
func: Callable[[EnvRunner], T],
*,
local_worker: bool = True,
healthy_only: bool = True,
remote_worker_ids: List[int] = None,
timeout_seconds: Optional[float] = None,
return_obj_refs: bool = False,
mark_healthy: bool = True,
) -> List[T]:
"""Calls the given function with each EnvRunner as its argument.
Args:
func: The function to call for each worker (as only arg).
local_worker: Whether apply `func` on local worker too. Default is True.
healthy_only: Apply `func` on known-to-be healthy workers only.
remote_worker_ids: Apply `func` on a selected set of remote workers. Use
None (default) for all remote EnvRunners.
timeout_seconds: Time to wait (in seconds) for results. Set this to 0.0 for
fire-and-forget. Set this to None (default) to wait infinitely (i.e. for
synchronous execution).
return_obj_refs: whether to return ObjectRef instead of actual results.
Note, for fault tolerance reasons, these returned ObjectRefs should
never be resolved with ray.get() outside of this WorkerSet.
mark_healthy: Whether to mark all those workers healthy again that are
currently marked unhealthy AND that returned results from the remote
call (within the given `timeout_seconds`).
Note that workers are NOT set unhealthy, if they simply time out
(only if they return a RayActorError).
Also not that this setting is ignored if `healthy_only=True` (b/c this
setting only affects workers that are currently tagged as unhealthy).
Returns:
The list of return values of all calls to `func([worker])`.
"""
assert (
not return_obj_refs or not local_worker
), "Can not return ObjectRef from local worker."
local_result = []
if local_worker and self.local_worker() is not None:
local_result = [func(self.local_worker())]
if not self._worker_manager.actor_ids():
return local_result
remote_results = self._worker_manager.foreach_actor(
func,
healthy_only=healthy_only,
remote_actor_ids=remote_worker_ids,
timeout_seconds=timeout_seconds,
return_obj_refs=return_obj_refs,
mark_healthy=mark_healthy,
)
_handle_remote_call_result_errors(
remote_results, self._ignore_env_runner_failures
)
# With application errors handled, return good results.
remote_results = [r.get() for r in remote_results.ignore_errors()]
return local_result + remote_results
[docs] @DeveloperAPI
def foreach_worker_with_id(
self,
func: Callable[[int, EnvRunner], T],
*,
local_worker: bool = True,
healthy_only: bool = True,
remote_worker_ids: List[int] = None,
timeout_seconds: Optional[float] = None,
) -> List[T]:
"""Calls the given function with each EnvRunner and its ID as its arguments.
Args:
func: The function to call for each worker (as only arg).
local_worker: Whether apply `func` on local worker too. Default is True.
healthy_only: Apply `func` on known-to-be healthy workers only.
remote_worker_ids: Apply `func` on a selected set of remote workers.
timeout_seconds: Time to wait for results. Default is None.
Returns:
The list of return values of all calls to `func([worker, id])`.
"""
local_result = []
if local_worker and self.local_worker() is not None:
local_result = [func(0, self.local_worker())]
if not remote_worker_ids:
remote_worker_ids = self._worker_manager.actor_ids()
funcs = [functools.partial(func, i) for i in remote_worker_ids]
remote_results = self._worker_manager.foreach_actor(
funcs,
healthy_only=healthy_only,
remote_actor_ids=remote_worker_ids,
timeout_seconds=timeout_seconds,
)
_handle_remote_call_result_errors(
remote_results, self._ignore_env_runner_failures
)
remote_results = [r.get() for r in remote_results.ignore_errors()]
return local_result + remote_results
[docs] @DeveloperAPI
def foreach_worker_async(
self,
func: Callable[[EnvRunner], T],
*,
healthy_only: bool = True,
remote_worker_ids: List[int] = None,
) -> int:
"""Calls the given function asynchronously with each worker as the argument.
foreach_worker_async() does not return results directly. Instead,
fetch_ready_async_reqs() can be used to pull results in an async manner
whenever they are available.
Args:
func: The function to call for each worker (as only arg).
healthy_only: Apply `func` on known-to-be healthy workers only.
remote_worker_ids: Apply `func` on a selected set of remote workers.
Returns:
The number of async requests that have actually been made. This is the
length of `remote_worker_ids` (or self.num_remote_workers()` if
`remote_worker_ids` is None) minus the number of requests that were NOT
made b/c a remote worker already had its
`max_remote_requests_in_flight_per_actor` counter reached.
"""
return self._worker_manager.foreach_actor_async(
func,
healthy_only=healthy_only,
remote_actor_ids=remote_worker_ids,
)
[docs] @DeveloperAPI
def fetch_ready_async_reqs(
self,
*,
timeout_seconds: Optional[float] = 0.0,
return_obj_refs: bool = False,
mark_healthy: bool = True,
) -> List[Tuple[int, T]]:
"""Get esults from outstanding asynchronous requests that are ready.
Args:
timeout_seconds: Time to wait for results. Default is 0, meaning
those requests that are already ready.
return_obj_refs: Whether to return ObjectRef instead of actual results.
mark_healthy: Whether to mark all those workers healthy again that are
currently marked unhealthy AND that returned results from the remote
call (within the given `timeout_seconds`).
Note that workers are NOT set unhealthy, if they simply time out
(only if they return a RayActorError).
Also not that this setting is ignored if `healthy_only=True` was set
in the preceding `self.foreach_worker_asyn()` call, b/c the
`mark_healthy` setting only affects workers that are currently tagged as
unhealthy.
Returns:
A list of results successfully returned from outstanding remote calls,
paired with the indices of the callee workers.
"""
remote_results = self._worker_manager.fetch_ready_async_reqs(
timeout_seconds=timeout_seconds,
return_obj_refs=return_obj_refs,
mark_healthy=mark_healthy,
)
_handle_remote_call_result_errors(
remote_results, self._ignore_env_runner_failures
)
return [(r.actor_id, r.get()) for r in remote_results.ignore_errors()]
[docs] @DeveloperAPI
def foreach_policy(self, func: Callable[[Policy, PolicyID], T]) -> List[T]:
"""Calls `func` with each worker's (policy, PolicyID) tuple.
Note that in the multi-agent case, each worker may have more than one
policy.
Args:
func: A function - taking a Policy and its ID - that is
called on all workers' Policies.
Returns:
The list of return values of func over all workers' policies. The
length of this list is:
(num_workers + 1 (local-worker)) *
[num policies in the multi-agent config dict].
The local workers' results are first, followed by all remote
workers' results
"""
results = []
for r in self.foreach_worker(
lambda w: w.foreach_policy(func), local_worker=True
):
results.extend(r)
return results
[docs] @DeveloperAPI
def foreach_policy_to_train(self, func: Callable[[Policy, PolicyID], T]) -> List[T]:
"""Apply `func` to all workers' Policies iff in `policies_to_train`.
Args:
func: A function - taking a Policy and its ID - that is
called on all workers' Policies, for which
`worker.is_policy_to_train()` returns True.
Returns:
List[any]: The list of n return values of all
`func([trainable policy], [ID])`-calls.
"""
results = []
for r in self.foreach_worker(
lambda w: w.foreach_policy_to_train(func), local_worker=True
):
results.extend(r)
return results
[docs] @DeveloperAPI
def foreach_env(self, func: Callable[[EnvType], List[T]]) -> List[List[T]]:
"""Calls `func` with all workers' sub-environments as args.
An "underlying sub environment" is a single clone of an env within
a vectorized environment.
`func` takes a single underlying sub environment as arg, e.g. a
gym.Env object.
Args:
func: A function - taking an EnvType (normally a gym.Env object)
as arg and returning a list of lists of return values, one
value per underlying sub-environment per each worker.
Returns:
The list (workers) of lists (sub environments) of results.
"""
return list(
self.foreach_worker(
lambda w: w.foreach_env(func),
local_worker=True,
)
)
[docs] @DeveloperAPI
def foreach_env_with_context(
self, func: Callable[[BaseEnv, EnvContext], List[T]]
) -> List[List[T]]:
"""Calls `func` with all workers' sub-environments and env_ctx as args.
An "underlying sub environment" is a single clone of an env within
a vectorized environment.
`func` takes a single underlying sub environment and the env_context
as args.
Args:
func: A function - taking a BaseEnv object and an EnvContext as
arg - and returning a list of lists of return values over envs
of the worker.
Returns:
The list (1 item per workers) of lists (1 item per sub-environment)
of results.
"""
return list(
self.foreach_worker(
lambda w: w.foreach_env_with_context(func),
local_worker=True,
)
)
[docs] @DeveloperAPI
def probe_unhealthy_workers(self) -> List[int]:
"""Checks for unhealthy workers and tries restoring their states.
Returns:
List of IDs of the workers that were restored.
"""
return self._worker_manager.probe_unhealthy_actors(
timeout_seconds=self._remote_config.env_runner_health_probe_timeout_s,
)
# TODO (sven): Deprecate once ARS/ES have been moved to `rllib_contrib`.
@staticmethod
def _from_existing(
local_worker: EnvRunner, remote_workers: List[ActorHandle] = None
):
workers = EnvRunnerGroup(
env_creator=None, default_policy_class=None, config=None, _setup=False
)
workers.reset(remote_workers or [])
workers._local_worker = local_worker
return workers
def _make_worker(
self,
*,
cls: Callable,
env_creator: EnvCreator,
validate_env: Optional[Callable[[EnvType], None]],
worker_index: int,
num_workers: int,
recreated_worker: bool = False,
config: "AlgorithmConfig",
spaces: Optional[
Dict[PolicyID, Tuple[gym.spaces.Space, gym.spaces.Space]]
] = None,
) -> Union[EnvRunner, ActorHandle]:
worker = cls(
env_creator=env_creator,
validate_env=validate_env,
default_policy_class=self._policy_class,
config=config,
worker_index=worker_index,
num_workers=num_workers,
recreated_worker=recreated_worker,
log_dir=self._logdir,
spaces=spaces,
dataset_shards=self._ds_shards,
tune_trial_id=self._tune_trial_id,
)
return worker
@classmethod
def _valid_module(cls, class_path):
del cls
if (
isinstance(class_path, str)
and not os.path.isfile(class_path)
and "." in class_path
):
module_path, class_name = class_path.rsplit(".", 1)
try:
spec = importlib.util.find_spec(module_path)
if spec is not None:
return True
except (ModuleNotFoundError, ValueError):
print(
f"module {module_path} not found while trying to get "
f"input {class_path}"
)
return False
@Deprecated(new="EnvRunnerGroup.foreach_policy_to_train", error=True)
def foreach_trainable_policy(self, func):
pass
@property
@Deprecated(
old="_remote_workers",
new="Use either the `foreach_worker()`, `foreach_worker_with_id()`, or "
"`foreach_worker_async()` APIs of `EnvRunnerGroup`, which all handle fault "
"tolerance.",
error=False,
)
def _remote_workers(self) -> List[ActorHandle]:
return list(self._worker_manager.actors().values())
@Deprecated(
old="remote_workers()",
new="Use either the `foreach_worker()`, `foreach_worker_with_id()`, or "
"`foreach_worker_async()` APIs of `EnvRunnerGroup`, which all handle fault "
"tolerance.",
error=False,
)
def remote_workers(self) -> List[ActorHandle]:
return list(self._worker_manager.actors().values())
def _handle_remote_call_result_errors(
results: RemoteCallResults,
ignore_env_runner_failures: bool,
) -> None:
"""Checks given results for application errors and raises them if necessary.
Args:
results: The results to check.
"""
for r in results.ignore_ray_errors():
if r.ok:
continue
if ignore_env_runner_failures:
logger.exception(r.get())
else:
raise r.get()