def _build_value_model(self, model_config: ModelConfigDict):
"""Build value model with given model configuration
model_config = {'activation': str, 'hiddens': Sequence}
"""
activation = get_activation_fn(model_config.get("activation"))
hiddens = model_config.get("hiddens", [])
inputs = tf.keras.layers.Input(
shape=(np.product(self.critic_preprocessor.shape),), name="value-inputs"
)
last_layer = inputs
for i, size in enumerate(hiddens):
last_layer = tf.keras.layers.Dense(
size,
name="fc_{}".format(i),
activation=activation,
kernel_initializer=normc_initializer(1.0),
)(last_layer)
value_out = tf.keras.layers.Dense(
1,
name="value_out",
activation=None,
kernel_initializer=normc_initializer(0.01),
)(last_layer)
return tf.keras.Model(inputs, [value_out])
def __init__(
self,
env_creator: Callable[[EnvContext], EnvType],
policy: type,
policy_mapping_fn: Callable[[AgentID], PolicyID] = None,
policies_to_train: List[PolicyID] = None,
tf_session_creator: Callable[[], Any] = None,
rollout_fragment_length: int = 100,
batch_mode: str = "truncate_episodes",
episode_horizon: int = None,
preprocessor_pref: str = "deepmind",
sample_async: bool = False,
compress_observations: bool = False,
num_envs: int = 1,
observation_fn: "ObservationFunction" = None,
observation_filter: str = "NoFilter",
clip_rewards: bool = None,
clip_actions: bool = True,
env_config: EnvConfigDict = None,
model_config: ModelConfigDict = None,
policy_config: TrainerConfigDict = None,
worker_index: int = 0,
num_workers: int = 0,
monitor_path: str = None,
log_dir: str = None,
log_level: str = None,
callbacks: "DefaultCallbacks" = None,
input_creator: Callable[
[IOContext],
InputReader] = lambda ioctx: ioctx.default_sampler_input(),
input_evaluation: List[str] = frozenset([]),
output_creator: Callable[
[IOContext], OutputWriter] = lambda ioctx: NoopOutput(),
remote_worker_envs: bool = False,
remote_env_batch_wait_ms: int = 0,
soft_horizon: bool = False,
no_done_at_end: bool = False,
seed: int = None,
extra_python_environs: dict = None,
fake_sampler: bool = False):
"""Initialize a rollout worker.
Arguments:
env_creator (func): Function that returns a gym.Env given an
EnvContext wrapped configuration.
policy (class|dict): Either a class implementing
Policy, or a dictionary of policy id strings to
(Policy, obs_space, action_space, config) tuples. If a
dict is specified, then we are in multi-agent mode and a
policy_mapping_fn should also be set.
policy_mapping_fn (func): A function that maps agent ids to
policy ids in multi-agent mode. This function will be called
each time a new agent appears in an episode, to bind that agent
to a policy for the duration of the episode.
policies_to_train (list): Optional list of policies to train,
or None for all policies.
tf_session_creator (func): A function that returns a TF session.
This is optional and only useful with TFPolicy.
rollout_fragment_length (int): The target number of env transitions
to include in each sample batch returned from this worker.
batch_mode (str): One of the following batch modes:
"truncate_episodes": Each call to sample() will return a batch
of at most `rollout_fragment_length * num_envs` in size.
The batch will be exactly
`rollout_fragment_length * num_envs` in size if
postprocessing does not change batch sizes. Episodes may be
truncated in order to meet this size requirement.
"complete_episodes": Each call to sample() will return a batch
of at least `rollout_fragment_length * num_envs` in size.
Episodes will not be truncated, but multiple episodes may
be packed within one batch to meet the batch size. Note
that when `num_envs > 1`, episode steps will be buffered
until the episode completes, and hence batches may contain
significant amounts of off-policy data.
episode_horizon (int): Whether to stop episodes at this horizon.
preprocessor_pref (str): Whether to prefer RLlib preprocessors
("rllib") or deepmind ("deepmind") when applicable.
sample_async (bool): Whether to compute samples asynchronously in
the background, which improves throughput but can cause samples
to be slightly off-policy.
compress_observations (bool): If true, compress the observations.
They can be decompressed with rllib/utils/compression.
num_envs (int): If more than one, will create multiple envs
and vectorize the computation of actions. This has no effect if
if the env already implements VectorEnv.
observation_fn (ObservationFunction): Optional multi-agent
observation function.
observation_filter (str): Name of observation filter to use.
clip_rewards (bool): Whether to clip rewards to [-1, 1] prior to
experience postprocessing. Setting to None means clip for Atari
only.
clip_actions (bool): Whether to clip action values to the range
specified by the policy action space.
env_config (dict): Config to pass to the env creator.
model_config (dict): Config to use when creating the policy model.
policy_config (dict): Config to pass to the policy. In the
multi-agent case, this config will be merged with the
per-policy configs specified by `policy`.
worker_index (int): For remote workers, this should be set to a
non-zero and unique value. This index is passed to created envs
through EnvContext so that envs can be configured per worker.
num_workers (int): For remote workers, how many workers altogether
have been created?
monitor_path (str): Write out episode stats and videos to this
directory if specified.
log_dir (str): Directory where logs can be placed.
log_level (str): Set the root log level on creation.
callbacks (DefaultCallbacks): Custom training callbacks.
input_creator (func): Function that returns an InputReader object
for loading previous generated experiences.
input_evaluation (list): How to evaluate the policy performance.
This only makes sense to set when the input is reading offline
data. The possible values include:
- "is": the step-wise importance sampling estimator.
- "wis": the weighted step-wise is estimator.
- "simulation": run the environment in the background, but
use this data for evaluation only and never for learning.
output_creator (func): Function that returns an OutputWriter object
for saving generated experiences.
remote_worker_envs (bool): If using num_envs > 1, whether to create
those new envs in remote processes instead of in the current
process. This adds overheads, but can make sense if your envs
remote_env_batch_wait_ms (float): Timeout that remote workers
are waiting when polling environments. 0 (continue when at
least one env is ready) is a reasonable default, but optimal
value could be obtained by measuring your environment
step / reset and model inference perf.
soft_horizon (bool): Calculate rewards but don't reset the
environment when the horizon is hit.
no_done_at_end (bool): Ignore the done=True at the end of the
episode and instead record done=False.
seed (int): Set the seed of both np and tf to this value to
to ensure each remote worker has unique exploration behavior.
extra_python_environs (dict): Extra python environments need to
be set.
fake_sampler (bool): Use a fake (inf speed) sampler for testing.
"""
self._original_kwargs: dict = locals().copy()
del self._original_kwargs["self"]
global _global_worker
_global_worker = self
# set extra environs first
if extra_python_environs:
for key, value in extra_python_environs.items():
os.environ[key] = str(value)
def gen_rollouts():
while True:
yield self.sample()
ParallelIteratorWorker.__init__(self, gen_rollouts, False)
policy_config: TrainerConfigDict = policy_config or {}
if (tf1 and policy_config.get("framework") in ["tf2", "tfe"]
and not policy_config.get("no_eager_on_workers")
# This eager check is necessary for certain all-framework tests
# that use tf's eager_mode() context generator.
and not tf1.executing_eagerly()):
tf1.enable_eager_execution()
if log_level:
logging.getLogger("ray.rllib").setLevel(log_level)
if worker_index > 1:
disable_log_once_globally() # only need 1 worker to log
elif log_level == "DEBUG":
enable_periodic_logging()
env_context = EnvContext(env_config or {}, worker_index)
self.env_context = env_context
self.policy_config: TrainerConfigDict = policy_config
if callbacks:
self.callbacks: "DefaultCallbacks" = callbacks()
else:
from ray.rllib.agents.callbacks import DefaultCallbacks
self.callbacks: "DefaultCallbacks" = DefaultCallbacks()
self.worker_index: int = worker_index
self.num_workers: int = num_workers
model_config: ModelConfigDict = model_config or {}
policy_mapping_fn = (policy_mapping_fn
or (lambda agent_id: DEFAULT_POLICY_ID))
if not callable(policy_mapping_fn):
raise ValueError("Policy mapping function not callable?")
self.env_creator: Callable[[EnvContext], EnvType] = env_creator
self.rollout_fragment_length: int = rollout_fragment_length * num_envs
self.batch_mode: str = batch_mode
self.compress_observations: bool = compress_observations
self.preprocessing_enabled: bool = True
self.last_batch: SampleBatchType = None
self.global_vars: dict = None
self.fake_sampler: bool = fake_sampler
self.env = _validate_env(env_creator(env_context))
if isinstance(self.env, (BaseEnv, MultiAgentEnv)):
def wrap(env):
return env # we can't auto-wrap these env types
elif is_atari(self.env) and \
not model_config.get("custom_preprocessor") and \
preprocessor_pref == "deepmind":
# Deepmind wrappers already handle all preprocessing
self.preprocessing_enabled = False
# If clip_rewards not explicitly set to False, switch it
# on here (clip between -1.0 and 1.0).
if clip_rewards is None:
clip_rewards = True
def wrap(env):
env = wrap_deepmind(env,
dim=model_config.get("dim"),
framestack=model_config.get("framestack"))
if monitor_path:
from gym import wrappers
env = wrappers.Monitor(env, monitor_path, resume=True)
return env
else:
def wrap(env):
if monitor_path:
from gym import wrappers
env = wrappers.Monitor(env, monitor_path, resume=True)
return env
self.env: EnvType = wrap(self.env)
def make_env(vector_index):
return wrap(
env_creator(
env_context.copy_with_overrides(
worker_index=worker_index,
vector_index=vector_index,
remote=remote_worker_envs)))
self.make_env_fn = make_env
self.tf_sess = None
policy_dict = _validate_and_canonicalize(policy, self.env)
self.policies_to_train: List[PolicyID] = policies_to_train or list(
policy_dict.keys())
self.policy_map: Dict[PolicyID, Policy] = None
self.preprocessors: Dict[PolicyID, Preprocessor] = None
# set numpy and python seed
if seed is not None:
np.random.seed(seed)
random.seed(seed)
if not hasattr(self.env, "seed"):
raise ValueError("Env doesn't support env.seed(): {}".format(
self.env))
self.env.seed(seed)
try:
assert torch is not None
torch.manual_seed(seed)
except AssertionError:
logger.info("Could not seed torch")
if _has_tensorflow_graph(policy_dict) and not (
tf1 and tf1.executing_eagerly()):
if not tf1:
raise ImportError("Could not import tensorflow")
with tf1.Graph().as_default():
if tf_session_creator:
self.tf_sess = tf_session_creator()
else:
self.tf_sess = tf1.Session(config=tf1.ConfigProto(
gpu_options=tf1.GPUOptions(allow_growth=True)))
with self.tf_sess.as_default():
# set graph-level seed
if seed is not None:
tf1.set_random_seed(seed)
self.policy_map, self.preprocessors = \
self._build_policy_map(policy_dict, policy_config)
else:
self.policy_map, self.preprocessors = self._build_policy_map(
policy_dict, policy_config)
if (ray.is_initialized()
and ray.worker._mode() != ray.worker.LOCAL_MODE):
# Check available number of GPUs
if not ray.get_gpu_ids(as_str=True):
logger.debug("Creating policy evaluation worker {}".format(
worker_index) +
" on CPU (please ignore any CUDA init errors)")
elif (policy_config["framework"] in ["tf2", "tf", "tfe"] and
not tf.config.experimental.list_physical_devices("GPU")) or \
(policy_config["framework"] == "torch" and
not torch.cuda.is_available()):
raise RuntimeError(
"GPUs were assigned to this worker by Ray, but "
"your DL framework ({}) reports GPU acceleration is "
"disabled. This could be due to a bad CUDA- or {} "
"installation.".format(policy_config["framework"],
policy_config["framework"]))
self.multiagent: bool = set(
self.policy_map.keys()) != {DEFAULT_POLICY_ID}
if self.multiagent:
if not ((isinstance(self.env, MultiAgentEnv)
or isinstance(self.env, ExternalMultiAgentEnv))
or isinstance(self.env, BaseEnv)):
raise ValueError(
"Have multiple policies {}, but the env ".format(
self.policy_map) +
"{} is not a subclass of BaseEnv, MultiAgentEnv or "
"ExternalMultiAgentEnv?".format(self.env))
self.filters: Dict[PolicyID, Filter] = {
policy_id: get_filter(observation_filter,
policy.observation_space.shape)
for (policy_id, policy) in self.policy_map.items()
}
if self.worker_index == 0:
logger.info("Built filter map: {}".format(self.filters))
self.num_envs: int = num_envs
if "custom_vector_env" in policy_config:
custom_vec_wrapper = policy_config["custom_vector_env"]
self.async_env = custom_vec_wrapper(self.env)
else:
# Always use vector env for consistency even if num_envs = 1.
self.async_env: BaseEnv = BaseEnv.to_base_env(
self.env,
make_env=make_env,
num_envs=num_envs,
remote_envs=remote_worker_envs,
remote_env_batch_wait_ms=remote_env_batch_wait_ms)
# `truncate_episodes`: Allow a batch to contain more than one episode
# (fragments) and always make the batch `rollout_fragment_length`
# long.
if self.batch_mode == "truncate_episodes":
pack = True
# `complete_episodes`: Never cut episodes and sampler will return
# exactly one (complete) episode per poll.
elif self.batch_mode == "complete_episodes":
rollout_fragment_length = float("inf")
pack = False
else:
raise ValueError("Unsupported batch mode: {}".format(
self.batch_mode))
self.io_context: IOContext = IOContext(log_dir, policy_config,
worker_index, self)
self.reward_estimators: OffPolicyEstimator = []
for method in input_evaluation:
if method == "simulation":
logger.warning(
"Requested 'simulation' input evaluation method: "
"will discard all sampler outputs and keep only metrics.")
sample_async = True
elif method == "is":
ise = ImportanceSamplingEstimator.create(self.io_context)
self.reward_estimators.append(ise)
elif method == "wis":
wise = WeightedImportanceSamplingEstimator.create(
self.io_context)
self.reward_estimators.append(wise)
else:
raise ValueError(
"Unknown evaluation method: {}".format(method))
if sample_async:
self.sampler = AsyncSampler(
worker=self,
env=self.async_env,
policies=self.policy_map,
policy_mapping_fn=policy_mapping_fn,
preprocessors=self.preprocessors,
obs_filters=self.filters,
clip_rewards=clip_rewards,
rollout_fragment_length=rollout_fragment_length,
callbacks=self.callbacks,
horizon=episode_horizon,
pack_multiple_episodes_in_batch=pack,
tf_sess=self.tf_sess,
clip_actions=clip_actions,
blackhole_outputs="simulation" in input_evaluation,
soft_horizon=soft_horizon,
no_done_at_end=no_done_at_end,
observation_fn=observation_fn,
_use_trajectory_view_api=policy_config.get(
"_use_trajectory_view_api", False))
# Start the Sampler thread.
self.sampler.start()
else:
self.sampler = SyncSampler(
worker=self,
env=self.async_env,
policies=self.policy_map,
policy_mapping_fn=policy_mapping_fn,
preprocessors=self.preprocessors,
obs_filters=self.filters,
clip_rewards=clip_rewards,
rollout_fragment_length=rollout_fragment_length,
callbacks=self.callbacks,
horizon=episode_horizon,
pack_multiple_episodes_in_batch=pack,
tf_sess=self.tf_sess,
clip_actions=clip_actions,
soft_horizon=soft_horizon,
no_done_at_end=no_done_at_end,
observation_fn=observation_fn,
_use_trajectory_view_api=policy_config.get(
"_use_trajectory_view_api", False))
self.input_reader: InputReader = input_creator(self.io_context)
self.output_writer: OutputWriter = output_creator(self.io_context)
logger.debug(
"Created rollout worker with env {} ({}), policies {}".format(
self.async_env, self.env, self.policy_map))
def get_model_v2(obs_space: gym.Space,
action_space: gym.Space,
num_outputs: int,
model_config: ModelConfigDict,
framework: str = "tf",
name: str = "default_model",
model_interface: type = None,
default_model: type = None,
**model_kwargs) -> ModelV2:
"""Returns a suitable model compatible with given spaces and output.
Args:
obs_space (Space): Observation space of the target gym env. This
may have an `original_space` attribute that specifies how to
unflatten the tensor into a ragged tensor.
action_space (Space): Action space of the target gym env.
num_outputs (int): The size of the output vector of the model.
framework (str): One of "tf", "tfe", or "torch".
name (str): Name (scope) for the model.
model_interface (cls): Interface required for the model
default_model (cls): Override the default class for the model. This
only has an effect when not using a custom model
model_kwargs (dict): args to pass to the ModelV2 constructor
Returns:
model (ModelV2): Model to use for the policy.
"""
if model_config.get("custom_model"):
if "custom_options" in model_config and \
model_config["custom_options"] != DEPRECATED_VALUE:
deprecation_warning("model.custom_options",
"model.custom_model_config",
error=False)
model_config["custom_model_config"] = \
model_config.pop("custom_options")
if isinstance(model_config["custom_model"], type):
model_cls = model_config["custom_model"]
else:
model_cls = _global_registry.get(RLLIB_MODEL,
model_config["custom_model"])
# TODO(sven): Hard-deprecate Model(V1).
if issubclass(model_cls, ModelV2):
logger.info("Wrapping {} as {}".format(model_cls,
model_interface))
model_cls = ModelCatalog._wrap_if_needed(
model_cls, model_interface)
if framework in ["tf", "tfe"]:
# Track and warn if vars were created but not registered.
created = set()
def track_var_creation(next_creator, **kw):
v = next_creator(**kw)
created.add(v)
return v
with tf.variable_creator_scope(track_var_creation):
# Try calling with kwargs first (custom ModelV2 should
# accept these as kwargs, not get them from
# config["custom_model_config"] anymore).
try:
instance = model_cls(obs_space, action_space,
num_outputs, model_config,
name, **model_kwargs)
except TypeError as e:
# Keyword error: Try old way w/o kwargs.
if "__init__() got an unexpected " in e.args[0]:
logger.warning(
"Custom ModelV2 should accept all custom "
"options as **kwargs, instead of expecting"
" them in config['custom_model_config']!")
instance = model_cls(obs_space, action_space,
num_outputs, model_config,
name)
# Other error -> re-raise.
else:
raise e
registered = set(instance.variables())
not_registered = set()
for var in created:
if var not in registered:
not_registered.add(var)
if not_registered:
raise ValueError(
"It looks like variables {} were created as part "
"of {} but does not appear in model.variables() "
"({}). Did you forget to call "
"model.register_variables() on the variables in "
"question?".format(not_registered, instance,
registered))
else:
# PyTorch automatically tracks nn.Modules inside the parent
# nn.Module's constructor.
# TODO(sven): Do this for TF as well.
instance = model_cls(obs_space, action_space, num_outputs,
model_config, name, **model_kwargs)
return instance
# TODO(sven): Hard-deprecate Model(V1). This check will be
# superflous then.
elif tf.executing_eagerly():
raise ValueError(
"Eager execution requires a TFModelV2 model to be "
"used, however you specified a custom model {}".format(
model_cls))
if framework in ["tf", "tfe", "tf2"]:
v2_class = None
# Try to get a default v2 model.
if not model_config.get("custom_model"):
v2_class = default_model or ModelCatalog._get_v2_model_class(
obs_space, model_config, framework=framework)
if model_config.get("use_lstm"):
wrapped_cls = v2_class
forward = wrapped_cls.forward
v2_class = ModelCatalog._wrap_if_needed(
wrapped_cls, LSTMWrapper)
v2_class._wrapped_forward = forward
# fallback to a default v1 model
if v2_class is None:
if tf.executing_eagerly():
raise ValueError(
"Eager execution requires a TFModelV2 model to be "
"used, however there is no default V2 model for this "
"observation space: {}, use_lstm={}".format(
obs_space, model_config.get("use_lstm")))
v2_class = make_v1_wrapper(ModelCatalog.get_model)
# Wrap in the requested interface.
wrapper = ModelCatalog._wrap_if_needed(v2_class, model_interface)
return wrapper(obs_space, action_space, num_outputs, model_config,
name, **model_kwargs)
elif framework == "torch":
v2_class = \
default_model or ModelCatalog._get_v2_model_class(
obs_space, model_config, framework=framework)
if model_config.get("use_lstm"):
from ray.rllib.models.torch.recurrent_net import LSTMWrapper \
as TorchLSTMWrapper
wrapped_cls = v2_class
forward = wrapped_cls.forward
v2_class = ModelCatalog._wrap_if_needed(
wrapped_cls, TorchLSTMWrapper)
v2_class._wrapped_forward = forward
# Wrap in the requested interface.
wrapper = ModelCatalog._wrap_if_needed(v2_class, model_interface)
return wrapper(obs_space, action_space, num_outputs, model_config,
name, **model_kwargs)
else:
raise NotImplementedError(
"`framework` must be 'tf|tfe|torch', but is "
"{}!".format(framework))
def get_action_dist(action_space: gym.Space,
config: ModelConfigDict,
dist_type: str = None,
framework: str = "tf",
**kwargs) -> (type, int):
"""Returns a distribution class and size for the given action space.
Args:
action_space (Space): Action space of the target gym env.
config (Optional[dict]): Optional model config.
dist_type (Optional[str]): Identifier of the action distribution
interpreted as a hint.
framework (str): One of "tf", "tfe", or "torch".
kwargs (dict): Optional kwargs to pass on to the Distribution's
constructor.
Returns:
Tuple:
- dist_class (ActionDistribution): Python class of the
distribution.
- dist_dim (int): The size of the input vector to the
distribution.
"""
dist = None
config = config or MODEL_DEFAULTS
# Custom distribution given.
if config.get("custom_action_dist"):
action_dist_name = config["custom_action_dist"]
logger.debug(
"Using custom action distribution {}".format(action_dist_name))
dist = _global_registry.get(RLLIB_ACTION_DIST, action_dist_name)
# Dist_type is given directly as a class.
elif type(dist_type) is type and \
issubclass(dist_type, ActionDistribution) and \
dist_type not in (
MultiActionDistribution, TorchMultiActionDistribution):
dist = dist_type
# Box space -> DiagGaussian OR Deterministic.
elif isinstance(action_space, gym.spaces.Box):
if len(action_space.shape) > 1:
raise UnsupportedSpaceException(
"Action space has multiple dimensions "
"{}. ".format(action_space.shape) +
"Consider reshaping this into a single dimension, "
"using a custom action distribution, "
"using a Tuple action space, or the multi-agent API.")
# TODO(sven): Check for bounds and return SquashedNormal, etc..
if dist_type is None:
dist = TorchDiagGaussian if framework == "torch" \
else DiagGaussian
elif dist_type == "deterministic":
dist = TorchDeterministic if framework == "torch" \
else Deterministic
# Discrete Space -> Categorical.
elif isinstance(action_space, gym.spaces.Discrete):
dist = TorchCategorical if framework == "torch" else Categorical
# Tuple/Dict Spaces -> MultiAction.
elif dist_type in (MultiActionDistribution,
TorchMultiActionDistribution) or \
isinstance(action_space, (gym.spaces.Tuple, gym.spaces.Dict)):
flat_action_space = flatten_space(action_space)
child_dists_and_in_lens = tree.map_structure(
lambda s: ModelCatalog.get_action_dist(
s, config, framework=framework), flat_action_space)
child_dists = [e[0] for e in child_dists_and_in_lens]
input_lens = [int(e[1]) for e in child_dists_and_in_lens]
return partial((TorchMultiActionDistribution if framework
== "torch" else MultiActionDistribution),
action_space=action_space,
child_distributions=child_dists,
input_lens=input_lens), int(sum(input_lens))
# Simplex -> Dirichlet.
elif isinstance(action_space, Simplex):
if framework == "torch":
# TODO(sven): implement
raise NotImplementedError(
"Simplex action spaces not supported for torch.")
dist = Dirichlet
# MultiDiscrete -> MultiCategorical.
elif isinstance(action_space, gym.spaces.MultiDiscrete):
dist = TorchMultiCategorical if framework == "torch" else \
MultiCategorical
return partial(dist, input_lens=action_space.nvec), \
int(sum(action_space.nvec))
# Unknown type -> Error.
else:
raise NotImplementedError("Unsupported args: {} {}".format(
action_space, dist_type))
return dist, dist.required_model_output_shape(action_space, config)