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ray/rllib/policy/policy.py

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from abc import ABCMeta, abstractmethod
import gym
from gym.spaces import Box
import logging
import numpy as np
import platform
import tree # pip install dm_tree
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Optional,
Tuple,
Type,
Union,
)
import ray
from ray.actor import ActorHandle
from ray.rllib.models.action_dist import ActionDistribution
from ray.rllib.models.catalog import ModelCatalog
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.view_requirement import ViewRequirement
from ray.rllib.utils.annotations import (
DeveloperAPI,
ExperimentalAPI,
OverrideToImplementCustomLogic,
OverrideToImplementCustomLogic_CallToSuperRecommended,
PublicAPI,
is_overridden,
)
from ray.rllib.utils.deprecation import Deprecated
from ray.rllib.utils.exploration.exploration import Exploration
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.from_config import from_config
from ray.rllib.utils.numpy import convert_to_numpy
from ray.rllib.utils.serialization import gym_space_from_dict, gym_space_to_dict
from ray.rllib.utils.spaces.space_utils import (
get_base_struct_from_space,
get_dummy_batch_for_space,
unbatch,
)
from ray.rllib.utils.typing import (
AgentID,
AlgorithmConfigDict,
ModelGradients,
ModelWeights,
PolicyID,
PolicyState,
T,
TensorStructType,
TensorType,
)
tf1, tf, tfv = try_import_tf()
torch, _ = try_import_torch()
if TYPE_CHECKING:
from ray.rllib.evaluation import Episode
logger = logging.getLogger(__name__)
@PublicAPI
class PolicySpec:
"""A policy spec used in the "config.multiagent.policies" specification dict.
As values (keys are the policy IDs (str)). E.g.:
config:
multiagent:
policies: {
"pol1": PolicySpec(None, Box, Discrete(2), {"lr": 0.0001}),
"pol2": PolicySpec(config={"lr": 0.001}),
}
"""
def __init__(
self, policy_class=None, observation_space=None, action_space=None, config=None
):
# If None, use the Algorithm's default policy class stored under
# `Algorithm._policy_class`.
self.policy_class = policy_class
# If None, use the env's observation space. If None and there is no Env
# (e.g. offline RL), an error is thrown.
self.observation_space = observation_space
# If None, use the env's action space. If None and there is no Env
# (e.g. offline RL), an error is thrown.
self.action_space = action_space
# Overrides defined keys in the main Algorithm config.
# If None, use {}.
self.config = config
def __eq__(self, other: "PolicySpec"):
return (
self.policy_class == other.policy_class
and self.observation_space == other.observation_space
and self.action_space == other.action_space
and self.config == other.config
)
def serialize(self) -> Dict:
return {
# TODO(jungong) : try making the policy_class config durable.
# Maybe we should save the full string class path instead.
# That will allow us to load a policy checkpoint even if the class
# does not exist anymore (e.g., renamed).
"policy_class": self.policy_class,
"observation_space": gym_space_to_dict(self.observation_space),
"action_space": gym_space_to_dict(self.action_space),
"config": self.config,
}
@classmethod
def deserialize(cls, spec: Dict) -> "PolicySpec":
return cls(
policy_class=spec["policy_class"],
observation_space=gym_space_from_dict(spec["observation_space"]),
action_space=gym_space_from_dict(spec["action_space"]),
config=spec["config"],
)
@DeveloperAPI
class Policy(metaclass=ABCMeta):
"""Policy base class: Calculates actions, losses, and holds NN models.
Policy is the abstract superclass for all DL-framework specific sub-classes
(e.g. TFPolicy or TorchPolicy). It exposes APIs to
1) Compute actions from observation (and possibly other) inputs.
2) Manage the Policy's NN model(s), like exporting and loading their
weights.
3) Postprocess a given trajectory from the environment or other input
via the `postprocess_trajectory` method.
4) Compute losses from a train batch.
5) Perform updates from a train batch on the NN-models (this normally
includes loss calculations) either a) in one monolithic step
(`train_on_batch`) or b) via batch pre-loading, then n steps of actual
loss computations and updates (`load_batch_into_buffer` +
`learn_on_loaded_batch`).
Note: It is not recommended to sub-class Policy directly, but rather use
one of the following two convenience methods:
`rllib.policy.policy_template::build_policy_class` (PyTorch) or
`rllib.policy.tf_policy_template::build_tf_policy_class` (TF).
"""
@DeveloperAPI
def __init__(
self,
observation_space: gym.Space,
action_space: gym.Space,
config: AlgorithmConfigDict,
):
"""Initializes a Policy instance.
Args:
observation_space: Observation space of the policy.
action_space: Action space of the policy.
config: A complete Algorithm/Policy config dict. For the default
config keys and values, see rllib/trainer/trainer.py.
"""
self.observation_space: gym.Space = observation_space
self.action_space: gym.Space = action_space
# The base struct of the observation/action spaces.
# E.g. action-space = gym.spaces.Dict({"a": Discrete(2)}) ->
# action_space_struct = {"a": Discrete(2)}
self.observation_space_struct = get_base_struct_from_space(observation_space)
self.action_space_struct = get_base_struct_from_space(action_space)
self.config: AlgorithmConfigDict = config
self.framework = self.config.get("framework")
# Create the callbacks object to use for handling custom callbacks.
if self.config.get("callbacks"):
self.callbacks: "DefaultCallbacks" = self.config.get("callbacks")()
else:
from ray.rllib.algorithms.callbacks import DefaultCallbacks
self.callbacks: "DefaultCallbacks" = DefaultCallbacks()
# The global timestep, broadcast down from time to time from the
# local worker to all remote workers.
self.global_timestep: int = 0
# The action distribution class to use for action sampling, if any.
# Child classes may set this.
self.dist_class: Optional[Type] = None
# Initialize view requirements.
self.init_view_requirements()
# Whether the Model's initial state (method) has been added
# automatically based on the given view requirements of the model.
self._model_init_state_automatically_added = False
# Connectors.
self.agent_connectors = None
self.action_connectors = None
@DeveloperAPI
def init_view_requirements(self):
"""Maximal view requirements dict for `learn_on_batch()` and
`compute_actions` calls.
Specific policies can override this function to provide custom
list of view requirements.
"""
# Maximal view requirements dict for `learn_on_batch()` and
# `compute_actions` calls.
# View requirements will be automatically filtered out later based
# on the postprocessing and loss functions to ensure optimal data
# collection and transfer performance.
view_reqs = self._get_default_view_requirements()
if not hasattr(self, "view_requirements"):
self.view_requirements = view_reqs
else:
for k, v in view_reqs.items():
if k not in self.view_requirements:
self.view_requirements[k] = v
@DeveloperAPI
def compute_single_action(
self,
obs: Optional[TensorStructType] = None,
state: Optional[List[TensorType]] = None,
*,
prev_action: Optional[TensorStructType] = None,
prev_reward: Optional[TensorStructType] = None,
info: dict = None,
input_dict: Optional[SampleBatch] = None,
episode: Optional["Episode"] = None,
explore: Optional[bool] = None,
timestep: Optional[int] = None,
# Kwars placeholder for future compatibility.
**kwargs,
) -> Tuple[TensorStructType, List[TensorType], Dict[str, TensorType]]:
"""Computes and returns a single (B=1) action value.
Takes an input dict (usually a SampleBatch) as its main data input.
This allows for using this method in case a more complex input pattern
(view requirements) is needed, for example when the Model requires the
last n observations, the last m actions/rewards, or a combination
of any of these.
Alternatively, in case no complex inputs are required, takes a single
`obs` values (and possibly single state values, prev-action/reward
values, etc..).
Args:
obs: Single observation.
state: List of RNN state inputs, if any.
prev_action: Previous action value, if any.
prev_reward: Previous reward, if any.
info: Info object, if any.
input_dict: A SampleBatch or input dict containing the
single (unbatched) Tensors to compute actions. If given, it'll
be used instead of `obs`, `state`, `prev_action|reward`, and
`info`.
episode: This provides access to all of the internal episode state,
which may be useful for model-based or multi-agent algorithms.
explore: Whether to pick an exploitation or
exploration action
(default: None -> use self.config["explore"]).
timestep: The current (sampling) time step.
Keyword Args:
kwargs: Forward compatibility placeholder.
Returns:
Tuple consisting of the action, the list of RNN state outputs (if
any), and a dictionary of extra features (if any).
"""
# Build the input-dict used for the call to
# `self.compute_actions_from_input_dict()`.
if input_dict is None:
input_dict = {SampleBatch.OBS: obs}
if state is not None:
for i, s in enumerate(state):
input_dict[f"state_in_{i}"] = s
if prev_action is not None:
input_dict[SampleBatch.PREV_ACTIONS] = prev_action
if prev_reward is not None:
input_dict[SampleBatch.PREV_REWARDS] = prev_reward
if info is not None:
input_dict[SampleBatch.INFOS] = info
# Batch all data in input dict.
input_dict = tree.map_structure_with_path(
lambda p, s: (
s
if p == "seq_lens"
else s.unsqueeze(0)
if torch and isinstance(s, torch.Tensor)
else np.expand_dims(s, 0)
),
input_dict,
)
episodes = None
if episode is not None:
episodes = [episode]
out = self.compute_actions_from_input_dict(
input_dict=SampleBatch(input_dict),
episodes=episodes,
explore=explore,
timestep=timestep,
)
# Some policies don't return a tuple, but always just a single action.
# E.g. ES and ARS.
if not isinstance(out, tuple):
single_action = out
state_out = []
info = {}
# Normal case: Policy should return (action, state, info) tuple.
else:
batched_action, state_out, info = out
single_action = unbatch(batched_action)
assert len(single_action) == 1
single_action = single_action[0]
# Return action, internal state(s), infos.
return (
single_action,
[s[0] for s in state_out],
{k: v[0] for k, v in info.items()},
)
@DeveloperAPI
def compute_actions_from_input_dict(
self,
input_dict: Union[SampleBatch, Dict[str, TensorStructType]],
explore: bool = None,
timestep: Optional[int] = None,
episodes: Optional[List["Episode"]] = None,
**kwargs,
) -> Tuple[TensorType, List[TensorType], Dict[str, TensorType]]:
"""Computes actions from collected samples (across multiple-agents).
Takes an input dict (usually a SampleBatch) as its main data input.
This allows for using this method in case a more complex input pattern
(view requirements) is needed, for example when the Model requires the
last n observations, the last m actions/rewards, or a combination
of any of these.
Args:
input_dict: A SampleBatch or input dict containing the Tensors
to compute actions. `input_dict` already abides to the
Policy's as well as the Model's view requirements and can
thus be passed to the Model as-is.
explore: Whether to pick an exploitation or exploration
action (default: None -> use self.config["explore"]).
timestep: The current (sampling) time step.
episodes: This provides access to all of the internal episodes'
state, which may be useful for model-based or multi-agent
algorithms.
Keyword Args:
kwargs: Forward compatibility placeholder.
Returns:
actions: Batch of output actions, with shape like
[BATCH_SIZE, ACTION_SHAPE].
state_outs: List of RNN state output
batches, if any, each with shape [BATCH_SIZE, STATE_SIZE].
info: Dictionary of extra feature batches, if any, with shape like
{"f1": [BATCH_SIZE, ...], "f2": [BATCH_SIZE, ...]}.
"""
# Default implementation just passes obs, prev-a/r, and states on to
# `self.compute_actions()`.
state_batches = [s for k, s in input_dict.items() if k[:9] == "state_in_"]
return self.compute_actions(
input_dict[SampleBatch.OBS],
state_batches,
prev_action_batch=input_dict.get(SampleBatch.PREV_ACTIONS),
prev_reward_batch=input_dict.get(SampleBatch.PREV_REWARDS),
info_batch=input_dict.get(SampleBatch.INFOS),
explore=explore,
timestep=timestep,
episodes=episodes,
**kwargs,
)
@abstractmethod
@DeveloperAPI
def compute_actions(
self,
obs_batch: Union[List[TensorStructType], TensorStructType],
state_batches: Optional[List[TensorType]] = None,
prev_action_batch: Union[List[TensorStructType], TensorStructType] = None,
prev_reward_batch: Union[List[TensorStructType], TensorStructType] = None,
info_batch: Optional[Dict[str, list]] = None,
episodes: Optional[List["Episode"]] = None,
explore: Optional[bool] = None,
timestep: Optional[int] = None,
**kwargs,
) -> Tuple[TensorType, List[TensorType], Dict[str, TensorType]]:
"""Computes actions for the current policy.
Args:
obs_batch: Batch of observations.
state_batches: List of RNN state input batches, if any.
prev_action_batch: Batch of previous action values.
prev_reward_batch: Batch of previous rewards.
info_batch: Batch of info objects.
episodes: List of Episode objects, one for each obs in
obs_batch. This provides access to all of the internal
episode state, which may be useful for model-based or
multi-agent algorithms.
explore: Whether to pick an exploitation or exploration action.
Set to None (default) for using the value of
`self.config["explore"]`.
timestep: The current (sampling) time step.
Keyword Args:
kwargs: Forward compatibility placeholder
Returns:
actions: Batch of output actions, with shape like
[BATCH_SIZE, ACTION_SHAPE].
state_outs (List[TensorType]): List of RNN state output
batches, if any, each with shape [BATCH_SIZE, STATE_SIZE].
info (List[dict]): Dictionary of extra feature batches, if any,
with shape like
{"f1": [BATCH_SIZE, ...], "f2": [BATCH_SIZE, ...]}.
"""
raise NotImplementedError
@DeveloperAPI
def compute_log_likelihoods(
self,
actions: Union[List[TensorType], TensorType],
obs_batch: Union[List[TensorType], TensorType],
state_batches: Optional[List[TensorType]] = None,
prev_action_batch: Optional[Union[List[TensorType], TensorType]] = None,
prev_reward_batch: Optional[Union[List[TensorType], TensorType]] = None,
actions_normalized: bool = True,
) -> TensorType:
"""Computes the log-prob/likelihood for a given action and observation.
The log-likelihood is calculated using this Policy's action
distribution class (self.dist_class).
Args:
actions: Batch of actions, for which to retrieve the
log-probs/likelihoods (given all other inputs: obs,
states, ..).
obs_batch: Batch of observations.
state_batches: List of RNN state input batches, if any.
prev_action_batch: Batch of previous action values.
prev_reward_batch: Batch of previous rewards.
actions_normalized: Is the given `actions` already normalized
(between -1.0 and 1.0) or not? If not and
`normalize_actions=True`, we need to normalize the given
actions first, before calculating log likelihoods.
Returns:
Batch of log probs/likelihoods, with shape: [BATCH_SIZE].
"""
raise NotImplementedError
@DeveloperAPI
@OverrideToImplementCustomLogic_CallToSuperRecommended
def postprocess_trajectory(
self,
sample_batch: SampleBatch,
other_agent_batches: Optional[
Dict[AgentID, Tuple["Policy", SampleBatch]]
] = None,
episode: Optional["Episode"] = None,
) -> SampleBatch:
"""Implements algorithm-specific trajectory postprocessing.
This will be called on each trajectory fragment computed during policy
evaluation. Each fragment is guaranteed to be only from one episode.
The given fragment may or may not contain the end of this episode,
depending on the `batch_mode=truncate_episodes|complete_episodes`,
`rollout_fragment_length`, and other settings.
Args:
sample_batch: batch of experiences for the policy,
which will contain at most one episode trajectory.
other_agent_batches: In a multi-agent env, this contains a
mapping of agent ids to (policy, agent_batch) tuples
containing the policy and experiences of the other agents.
episode: An optional multi-agent episode object to provide
access to all of the internal episode state, which may
be useful for model-based or multi-agent algorithms.
Returns:
The postprocessed sample batch.
"""
# The default implementation just returns the same, unaltered batch.
return sample_batch
@ExperimentalAPI
@OverrideToImplementCustomLogic
def loss(
self, model: ModelV2, dist_class: ActionDistribution, train_batch: SampleBatch
) -> Union[TensorType, List[TensorType]]:
"""Loss function for this Policy.
Override this method in order to implement custom loss computations.
Args:
model: The model to calculate the loss(es).
dist_class: The action distribution class to sample actions
from the model's outputs.
train_batch: The input batch on which to calculate the loss.
Returns:
Either a single loss tensor or a list of loss tensors.
"""
raise NotImplementedError
@DeveloperAPI
def learn_on_batch(self, samples: SampleBatch) -> Dict[str, TensorType]:
"""Perform one learning update, given `samples`.
Either this method or the combination of `compute_gradients` and
`apply_gradients` must be implemented by subclasses.
Args:
samples: The SampleBatch object to learn from.
Returns:
Dictionary of extra metadata from `compute_gradients()`.
Examples:
>>> policy, sample_batch = ... # doctest: +SKIP
>>> policy.learn_on_batch(sample_batch) # doctest: +SKIP
"""
# The default implementation is simply a fused `compute_gradients` plus
# `apply_gradients` call.
grads, grad_info = self.compute_gradients(samples)
self.apply_gradients(grads)
return grad_info
@ExperimentalAPI
def learn_on_batch_from_replay_buffer(
self, replay_actor: ActorHandle, policy_id: PolicyID
) -> Dict[str, TensorType]:
"""Samples a batch from given replay actor and performs an update.
Args:
replay_actor: The replay buffer actor to sample from.
policy_id: The ID of this policy.
Returns:
Dictionary of extra metadata from `compute_gradients()`.
"""
# Sample a batch from the given replay actor.
# Note that for better performance (less data sent through the
# network), this policy should be co-located on the same node
# as `replay_actor`. Such a co-location step is usually done during
# the Algorithm's `setup()` phase.
batch = ray.get(replay_actor.replay.remote(policy_id=policy_id))
if batch is None:
return {}
# Send to own learn_on_batch method for updating.
# TODO: hack w/ `hasattr`
if hasattr(self, "devices") and len(self.devices) > 1:
self.load_batch_into_buffer(batch, buffer_index=0)
return self.learn_on_loaded_batch(offset=0, buffer_index=0)
else:
return self.learn_on_batch(batch)
@DeveloperAPI
def load_batch_into_buffer(self, batch: SampleBatch, buffer_index: int = 0) -> int:
"""Bulk-loads the given SampleBatch into the devices' memories.
The data is split equally across all the Policy's devices.
If the data is not evenly divisible by the batch size, excess data
should be discarded.
Args:
batch: The SampleBatch to load.
buffer_index: The index of the buffer (a MultiGPUTowerStack) to use
on the devices. The number of buffers on each device depends
on the value of the `num_multi_gpu_tower_stacks` config key.
Returns:
The number of tuples loaded per device.
"""
raise NotImplementedError
@DeveloperAPI
def get_num_samples_loaded_into_buffer(self, buffer_index: int = 0) -> int:
"""Returns the number of currently loaded samples in the given buffer.
Args:
buffer_index: The index of the buffer (a MultiGPUTowerStack)
to use on the devices. The number of buffers on each device
depends on the value of the `num_multi_gpu_tower_stacks` config
key.
Returns:
The number of tuples loaded per device.
"""
raise NotImplementedError
@DeveloperAPI
def learn_on_loaded_batch(self, offset: int = 0, buffer_index: int = 0):
"""Runs a single step of SGD on an already loaded data in a buffer.
Runs an SGD step over a slice of the pre-loaded batch, offset by
the `offset` argument (useful for performing n minibatch SGD
updates repeatedly on the same, already pre-loaded data).
Updates the model weights based on the averaged per-device gradients.
Args:
offset: Offset into the preloaded data. Used for pre-loading
a train-batch once to a device, then iterating over
(subsampling through) this batch n times doing minibatch SGD.
buffer_index: The index of the buffer (a MultiGPUTowerStack)
to take the already pre-loaded data from. The number of buffers
on each device depends on the value of the
`num_multi_gpu_tower_stacks` config key.
Returns:
The outputs of extra_ops evaluated over the batch.
"""
raise NotImplementedError
@DeveloperAPI
def compute_gradients(
self, postprocessed_batch: SampleBatch
) -> Tuple[ModelGradients, Dict[str, TensorType]]:
"""Computes gradients given a batch of experiences.
Either this in combination with `apply_gradients()` or
`learn_on_batch()` must be implemented by subclasses.
Args:
postprocessed_batch: The SampleBatch object to use
for calculating gradients.
Returns:
grads: List of gradient output values.
grad_info: Extra policy-specific info values.
"""
raise NotImplementedError
@DeveloperAPI
def apply_gradients(self, gradients: ModelGradients) -> None:
"""Applies the (previously) computed gradients.
Either this in combination with `compute_gradients()` or
`learn_on_batch()` must be implemented by subclasses.
Args:
gradients: The already calculated gradients to apply to this
Policy.
"""
raise NotImplementedError
@DeveloperAPI
def get_weights(self) -> ModelWeights:
"""Returns model weights.
Note: The return value of this method will reside under the "weights"
key in the return value of Policy.get_state(). Model weights are only
one part of a Policy's state. Other state information contains:
optimizer variables, exploration state, and global state vars such as
the sampling timestep.
Returns:
Serializable copy or view of model weights.
"""
raise NotImplementedError
@DeveloperAPI
def set_weights(self, weights: ModelWeights) -> None:
"""Sets this Policy's model's weights.
Note: Model weights are only one part of a Policy's state. Other
state information contains: optimizer variables, exploration state,
and global state vars such as the sampling timestep.
Args:
weights: Serializable copy or view of model weights.
"""
raise NotImplementedError
@DeveloperAPI
def get_exploration_state(self) -> Dict[str, TensorType]:
"""Returns the state of this Policy's exploration component.
Returns:
Serializable information on the `self.exploration` object.
"""
return self.exploration.get_state()
@DeveloperAPI
def is_recurrent(self) -> bool:
"""Whether this Policy holds a recurrent Model.
Returns:
True if this Policy has-a RNN-based Model.
"""
return False
@DeveloperAPI
def num_state_tensors(self) -> int:
"""The number of internal states needed by the RNN-Model of the Policy.
Returns:
int: The number of RNN internal states kept by this Policy's Model.
"""
return 0
@DeveloperAPI
def get_initial_state(self) -> List[TensorType]:
"""Returns initial RNN state for the current policy.
Returns:
List[TensorType]: Initial RNN state for the current policy.
"""
return []
@DeveloperAPI
def get_state(self) -> PolicyState:
"""Returns the entire current state of this Policy.
Note: Not to be confused with an RNN model's internal state.
State includes the Model(s)' weights, optimizer weights,
the exploration component's state, as well as global variables, such
as sampling timesteps.
Returns:
Serialized local state.
"""
state = {
# All the policy's weights.
"weights": self.get_weights(),
# The current global timestep.
"global_timestep": self.global_timestep,
}
return state
@DeveloperAPI
def set_state(self, state: PolicyState) -> None:
"""Restores the entire current state of this Policy from `state`.
Args:
state: The new state to set this policy to. Can be
obtained by calling `self.get_state()`.
"""
self.set_weights(state["weights"])
self.global_timestep = state["global_timestep"]
@ExperimentalAPI
def apply(
self,
func: Callable[["Policy", Optional[Any], Optional[Any]], T],
*args,
**kwargs,
) -> T:
"""Calls the given function with this Policy instance.
Useful for when the Policy class has been converted into a ActorHandle
and the user needs to execute some functionality (e.g. add a property)
on the underlying policy object.
Args:
func: The function to call, with this Policy as first
argument, followed by args, and kwargs.
args: Optional additional args to pass to the function call.
kwargs: Optional additional kwargs to pass to the function call.
Returns:
The return value of the function call.
"""
return func(self, *args, **kwargs)
@DeveloperAPI
def on_global_var_update(self, global_vars: Dict[str, TensorType]) -> None:
"""Called on an update to global vars.
Args:
global_vars: Global variables by str key, broadcast from the
driver.
"""
# Store the current global time step (sum over all policies' sample
# steps).
# Make sure, we keep global_timestep as a Tensor for tf-eager
# (leads to memory leaks if not doing so).
if self.framework in ["tfe", "tf2"]:
self.global_timestep.assign(global_vars["timestep"])
else:
self.global_timestep = global_vars["timestep"]
@DeveloperAPI
def export_checkpoint(self, export_dir: str) -> None:
"""Export Policy checkpoint to local directory.
Args:
export_dir: Local writable directory.
"""
raise NotImplementedError
@DeveloperAPI
def export_model(self, export_dir: str, onnx: Optional[int] = None) -> None:
"""Exports the Policy's Model to local directory for serving.
Note: The file format will depend on the deep learning framework used.
See the child classed of Policy and their `export_model`
implementations for more details.
Args:
export_dir: Local writable directory.
onnx: If given, will export model in ONNX format. The
value of this parameter set the ONNX OpSet version to use.
"""
raise NotImplementedError
@DeveloperAPI
def import_model_from_h5(self, import_file: str) -> None:
"""Imports Policy from local file.
Args:
import_file: Local readable file.
"""
raise NotImplementedError
@DeveloperAPI
def get_session(self) -> Optional["tf1.Session"]:
"""Returns tf.Session object to use for computing actions or None.
Note: This method only applies to TFPolicy sub-classes. All other
sub-classes should expect a None to be returned from this method.
Returns:
The tf Session to use for computing actions and losses with
this policy or None.
"""
return None
def get_host(self) -> str:
"""Returns the computer's network name.
Returns:
The computer's networks name or an empty string, if the network
name could not be determined.
"""
return platform.node()
def _get_num_gpus_for_policy(self) -> int:
"""Decide on the number of CPU/GPU nodes this policy should run on.
Return:
0 if policy should run on CPU. >0 if policy should run on 1 or
more GPUs.
"""
worker_idx = self.config.get("worker_index", 0)
fake_gpus = self.config.get("_fake_gpus", False)
if (
ray._private.worker._mode() == ray._private.worker.LOCAL_MODE
and not fake_gpus
):
# If in local debugging mode, and _fake_gpus is not on.
num_gpus = 0
elif worker_idx == 0:
# If head node, take num_gpus.
num_gpus = self.config["num_gpus"]
else:
# If worker node, take num_gpus_per_worker
num_gpus = self.config["num_gpus_per_worker"]
if num_gpus == 0:
dev = "CPU"
else:
dev = "{} {}".format(num_gpus, "fake-GPUs" if fake_gpus else "GPUs")
logger.info(
"Policy (worker={}) running on {}.".format(
worker_idx if worker_idx > 0 else "local", dev
)
)
return num_gpus
def _create_exploration(self) -> Exploration:
"""Creates the Policy's Exploration object.
This method only exists b/c some Trainers do not use TfPolicy nor
TorchPolicy, but inherit directly from Policy. Others inherit from
TfPolicy w/o using DynamicTFPolicy.
TODO(sven): unify these cases.
Returns:
Exploration: The Exploration object to be used by this Policy.
"""
if getattr(self, "exploration", None) is not None:
return self.exploration
exploration = from_config(
Exploration,
self.config.get("exploration_config", {"type": "StochasticSampling"}),
action_space=self.action_space,
policy_config=self.config,
model=getattr(self, "model", None),
num_workers=self.config.get("num_workers", 0),
worker_index=self.config.get("worker_index", 0),
framework=getattr(self, "framework", self.config.get("framework", "tf")),
)
return exploration
def _get_default_view_requirements(self):
"""Returns a default ViewRequirements dict.
Note: This is the base/maximum requirement dict, from which later
some requirements will be subtracted again automatically to streamline
data collection, batch creation, and data transfer.
Returns:
ViewReqDict: The default view requirements dict.
"""
# Default view requirements (equal to those that we would use before
# the trajectory view API was introduced).
return {
SampleBatch.OBS: ViewRequirement(space=self.observation_space),
SampleBatch.NEXT_OBS: ViewRequirement(
data_col=SampleBatch.OBS, shift=1, space=self.observation_space
),
SampleBatch.ACTIONS: ViewRequirement(
space=self.action_space, used_for_compute_actions=False
),
# For backward compatibility with custom Models that don't specify
# these explicitly (will be removed by Policy if not used).
SampleBatch.PREV_ACTIONS: ViewRequirement(
data_col=SampleBatch.ACTIONS, shift=-1, space=self.action_space
),
SampleBatch.REWARDS: ViewRequirement(),
# For backward compatibility with custom Models that don't specify
# these explicitly (will be removed by Policy if not used).
SampleBatch.PREV_REWARDS: ViewRequirement(
data_col=SampleBatch.REWARDS, shift=-1
),
SampleBatch.DONES: ViewRequirement(),
SampleBatch.INFOS: ViewRequirement(),
SampleBatch.T: ViewRequirement(),
SampleBatch.EPS_ID: ViewRequirement(),
SampleBatch.UNROLL_ID: ViewRequirement(),
SampleBatch.AGENT_INDEX: ViewRequirement(),
SampleBatch.T: ViewRequirement(),
}
def _initialize_loss_from_dummy_batch(
self,
auto_remove_unneeded_view_reqs: bool = True,
stats_fn=None,
) -> None:
"""Performs test calls through policy's model and loss.
NOTE: This base method should work for define-by-run Policies such as
torch and tf-eager policies.
If required, will thereby detect automatically, which data views are
required by a) the forward pass, b) the postprocessing, and c) the loss
functions, and remove those from self.view_requirements that are not
necessary for these computations (to save data storage and transfer).
Args:
auto_remove_unneeded_view_reqs: Whether to automatically
remove those ViewRequirements records from
self.view_requirements that are not needed.
stats_fn (Optional[Callable[[Policy, SampleBatch], Dict[str,
TensorType]]]): An optional stats function to be called after
the loss.
"""
# Signal Policy that currently we do not like to eager/jit trace
# any function calls. This is to be able to track, which columns
# in the dummy batch are accessed by the different function (e.g.
# loss) such that we can then adjust our view requirements.
self._no_tracing = True
# Save for later so that loss init does not change global timestep
global_ts_before_init = int(convert_to_numpy(self.global_timestep))
sample_batch_size = max(self.batch_divisibility_req * 4, 32)
self._dummy_batch = self._get_dummy_batch_from_view_requirements(
sample_batch_size
)
self._lazy_tensor_dict(self._dummy_batch)
actions, state_outs, extra_outs = self.compute_actions_from_input_dict(
self._dummy_batch, explore=False
)
for key, view_req in self.view_requirements.items():
if key not in self._dummy_batch.accessed_keys:
view_req.used_for_compute_actions = False
# Add all extra action outputs to view reqirements (these may be
# filtered out later again, if not needed for postprocessing or loss).
for key, value in extra_outs.items():
self._dummy_batch[key] = value
if key not in self.view_requirements:
self.view_requirements[key] = ViewRequirement(
space=gym.spaces.Box(
-1.0, 1.0, shape=value.shape[1:], dtype=value.dtype
),
used_for_compute_actions=False,
)
for key in self._dummy_batch.accessed_keys:
if key not in self.view_requirements:
self.view_requirements[key] = ViewRequirement()
self.view_requirements[key].used_for_compute_actions = True
self._dummy_batch = self._get_dummy_batch_from_view_requirements(
sample_batch_size
)
self._dummy_batch.set_get_interceptor(None)
self.exploration.postprocess_trajectory(self, self._dummy_batch)
postprocessed_batch = self.postprocess_trajectory(self._dummy_batch)
seq_lens = None
if state_outs:
B = 4 # For RNNs, have B=4, T=[depends on sample_batch_size]
i = 0
while "state_in_{}".format(i) in postprocessed_batch:
postprocessed_batch["state_in_{}".format(i)] = postprocessed_batch[
"state_in_{}".format(i)
][:B]
if "state_out_{}".format(i) in postprocessed_batch:
postprocessed_batch["state_out_{}".format(i)] = postprocessed_batch[
"state_out_{}".format(i)
][:B]
i += 1
seq_len = sample_batch_size // B
seq_lens = np.array([seq_len for _ in range(B)], dtype=np.int32)
postprocessed_batch[SampleBatch.SEQ_LENS] = seq_lens
# Switch on lazy to-tensor conversion on `postprocessed_batch`.
train_batch = self._lazy_tensor_dict(postprocessed_batch)
# Calling loss, so set `is_training` to True.
train_batch.set_training(True)
if seq_lens is not None:
train_batch[SampleBatch.SEQ_LENS] = seq_lens
train_batch.count = self._dummy_batch.count
# Call the loss function, if it exists.
# TODO(jungong) : clean up after all agents get migrated.
# We should simply do self.loss(...) here.
if self._loss is not None:
self._loss(self, self.model, self.dist_class, train_batch)
elif is_overridden(self.loss) and not self.config["in_evaluation"]:
self.loss(self.model, self.dist_class, train_batch)
# Call the stats fn, if given.
# TODO(jungong) : clean up after all agents get migrated.
# We should simply do self.stats_fn(train_batch) here.
if stats_fn is not None:
stats_fn(self, train_batch)
if hasattr(self, "stats_fn") and not self.config["in_evaluation"]:
self.stats_fn(train_batch)
# Re-enable tracing.
self._no_tracing = False
# Add new columns automatically to view-reqs.
if auto_remove_unneeded_view_reqs:
# Add those needed for postprocessing and training.
all_accessed_keys = (
train_batch.accessed_keys
| self._dummy_batch.accessed_keys
| self._dummy_batch.added_keys
)
for key in all_accessed_keys:
if key not in self.view_requirements and key != SampleBatch.SEQ_LENS:
self.view_requirements[key] = ViewRequirement(
used_for_compute_actions=False
)
if self._loss or is_overridden(self.loss):
# Tag those only needed for post-processing (with some
# exceptions).
for key in self._dummy_batch.accessed_keys:
if (
key not in train_batch.accessed_keys
and key in self.view_requirements
and key not in self.model.view_requirements
and key
not in [
SampleBatch.EPS_ID,
SampleBatch.AGENT_INDEX,
SampleBatch.UNROLL_ID,
SampleBatch.DONES,
SampleBatch.REWARDS,
SampleBatch.INFOS,
SampleBatch.T,
]
):
self.view_requirements[key].used_for_training = False
# Remove those not needed at all (leave those that are needed
# by Sampler to properly execute sample collection).
# Also always leave DONES, REWARDS, INFOS, no matter what.
for key in list(self.view_requirements.keys()):
if (
key not in all_accessed_keys
and key
not in [
SampleBatch.EPS_ID,
SampleBatch.AGENT_INDEX,
SampleBatch.UNROLL_ID,
SampleBatch.DONES,
SampleBatch.REWARDS,
SampleBatch.INFOS,
SampleBatch.T,
]
and key not in self.model.view_requirements
):
# If user deleted this key manually in postprocessing
# fn, warn about it and do not remove from
# view-requirements.
if key in self._dummy_batch.deleted_keys:
logger.warning(
"SampleBatch key '{}' was deleted manually in "
"postprocessing function! RLlib will "
"automatically remove non-used items from the "
"data stream. Remove the `del` from your "
"postprocessing function.".format(key)
)
# If we are not writing output to disk, save to erase
# this key to save space in the sample batch.
elif self.config["output"] is None:
del self.view_requirements[key]
if type(self.global_timestep) is int:
self.global_timestep = global_ts_before_init
elif isinstance(self.global_timestep, tf.Variable):
self.global_timestep.assign(global_ts_before_init)
else:
raise ValueError(
"Variable self.global_timestep of policy {} needs to be "
"either of type `int` or `tf.Variable`, "
"but is of type {}.".format(self, type(self.global_timestep))
)
def _get_dummy_batch_from_view_requirements(
self, batch_size: int = 1
) -> SampleBatch:
"""Creates a numpy dummy batch based on the Policy's view requirements.
Args:
batch_size: The size of the batch to create.
Returns:
Dict[str, TensorType]: The dummy batch containing all zero values.
"""
ret = {}
for view_col, view_req in self.view_requirements.items():
data_col = view_req.data_col or view_col
# Flattened dummy batch.
if (isinstance(view_req.space, (gym.spaces.Tuple, gym.spaces.Dict))) and (
(
data_col == SampleBatch.OBS
and not self.config["_disable_preprocessor_api"]
)
or (
data_col == SampleBatch.ACTIONS
and not self.config.get("_disable_action_flattening")
)
):
_, shape = ModelCatalog.get_action_shape(
view_req.space, framework=self.config["framework"]
)
ret[view_col] = np.zeros((batch_size,) + shape[1:], np.float32)
# Non-flattened dummy batch.
else:
# Range of indices on time-axis, e.g. "-50:-1".
if view_req.shift_from is not None:
ret[view_col] = get_dummy_batch_for_space(
view_req.space,
batch_size=batch_size,
time_size=view_req.shift_to - view_req.shift_from + 1,
)
# Sequence of (probably non-consecutive) indices.
elif isinstance(view_req.shift, (list, tuple)):
ret[view_col] = get_dummy_batch_for_space(
view_req.space,
batch_size=batch_size,
time_size=len(view_req.shift),
)
# Single shift int value.
else:
if isinstance(view_req.space, gym.spaces.Space):
ret[view_col] = get_dummy_batch_for_space(
view_req.space, batch_size=batch_size, fill_value=0.0
)
else:
ret[view_col] = [view_req.space for _ in range(batch_size)]
# Due to different view requirements for the different columns,
# columns in the resulting batch may not all have the same batch size.
return SampleBatch(ret)
def _update_model_view_requirements_from_init_state(self):
"""Uses Model's (or this Policy's) init state to add needed ViewReqs.
Can be called from within a Policy to make sure RNNs automatically
update their internal state-related view requirements.
Changes the `self.view_requirements` dict.
"""
self._model_init_state_automatically_added = True
model = getattr(self, "model", None)
obj = model or self
if model and not hasattr(model, "view_requirements"):
model.view_requirements = {
SampleBatch.OBS: ViewRequirement(space=self.observation_space)
}
view_reqs = obj.view_requirements
# Add state-ins to this model's view.
init_state = []
if hasattr(obj, "get_initial_state") and callable(obj.get_initial_state):
init_state = obj.get_initial_state()
else:
# Add this functionality automatically for new native model API.
if (
tf
and isinstance(model, tf.keras.Model)
and "state_in_0" not in view_reqs
):
obj.get_initial_state = lambda: [
np.zeros_like(view_req.space.sample())
for k, view_req in model.view_requirements.items()
if k.startswith("state_in_")
]
else:
obj.get_initial_state = lambda: []
if "state_in_0" in view_reqs:
self.is_recurrent = lambda: True
# Make sure auto-generated init-state view requirements get added
# to both Policy and Model, no matter what.
view_reqs = [view_reqs] + (
[self.view_requirements] if hasattr(self, "view_requirements") else []
)
for i, state in enumerate(init_state):
# Allow `state` to be either a Space (use zeros as initial values)
# or any value (e.g. a dict or a non-zero tensor).
fw = (
np
if isinstance(state, np.ndarray)
else torch
if torch and torch.is_tensor(state)
else None
)
if fw:
space = (
Box(-1.0, 1.0, shape=state.shape) if fw.all(state == 0.0) else state
)
else:
space = state
for vr in view_reqs:
# Only override if user has not already provided
# custom view-requirements for state_in_n.
if "state_in_{}".format(i) not in vr:
vr["state_in_{}".format(i)] = ViewRequirement(
"state_out_{}".format(i),
shift=-1,
used_for_compute_actions=True,
batch_repeat_value=self.config.get("model", {}).get(
"max_seq_len", 1
),
space=space,
)
# Only override if user has not already provided
# custom view-requirements for state_out_n.
if "state_out_{}".format(i) not in vr:
vr["state_out_{}".format(i)] = ViewRequirement(
space=space, used_for_training=True
)
@DeveloperAPI
def __repr__(self):
return type(self).__name__
@Deprecated(new="get_exploration_state", error=False)
def get_exploration_info(self) -> Dict[str, TensorType]:
return self.get_exploration_state()
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