Source code for ray.rllib.policy.policy

from abc import ABCMeta, abstractmethod
from collections import namedtuple
import gym
from gym.spaces import Box
import logging
import numpy as np
from typing import Dict, List, Optional, TYPE_CHECKING

from ray.rllib.models.catalog import ModelCatalog
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.view_requirement import ViewRequirement
from ray.rllib.utils.annotations import Deprecated, DeveloperAPI
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.spaces.space_utils import clip_action, \
    get_base_struct_from_space, get_dummy_batch_for_space, unbatch, \
    unsquash_action
from ray.rllib.utils.typing import AgentID, ModelGradients, ModelWeights, \
    TensorType, TrainerConfigDict, Tuple, Union

tf1, tf, tfv = try_import_tf()
torch, _ = try_import_torch()

if TYPE_CHECKING:
    from ray.rllib.evaluation import MultiAgentEpisode

logger = logging.getLogger(__name__)

# By convention, metrics from optimizing the loss can be reported in the
# `grad_info` dict returned by learn_on_batch() / compute_grads() via this key.
LEARNER_STATS_KEY = "learner_stats"

# 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}),
#     }
PolicySpec = namedtuple(
    "PolicySpec",
    [
        # If None, use the Trainer's default policy class stored under
        # `Trainer._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.
        "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.
        "action_space",
        # Overrides defined keys in the main Trainer config.
        # If None, use {}.
        "config",
    ])  # defaults=(None, None, None, None)
# TODO: From 3.7 on, we could pass `defaults` into the above constructor.
#  We still support py3.6.
PolicySpec.__new__.__defaults__ = (None, None, None, None)


[docs]@DeveloperAPI class Policy(metaclass=ABCMeta): """An agent policy and loss, i.e., a TFPolicy or other subclass. This object defines how to act in the environment, and also losses used to improve the policy based on its experiences. Note that both policy and loss are defined together for convenience, though the policy itself is logically separate. All policies can directly extend Policy, however TensorFlow users may find TFPolicy simpler to implement. TFPolicy also enables RLlib to apply TensorFlow-specific optimizations such as fusing multiple policy graphs and multi-GPU support. Attributes: observation_space (gym.Space): Observation space of the policy. For complex spaces (e.g., Dict), this will be flattened version of the space, and you can access the original space via ``observation_space.original_space``. action_space (gym.Space): Action space of the policy. exploration (Exploration): The exploration object to use for computing actions, or None. """ @DeveloperAPI def __init__(self, observation_space: gym.spaces.Space, action_space: gym.spaces.Space, config: TrainerConfigDict): """Initialize the graph. This is the standard constructor for policies. The policy class you pass into RolloutWorker will be constructed with these arguments. Args: observation_space (gym.spaces.Space): Observation space of the policy. action_space (gym.spaces.Space): Action space of the policy. config (TrainerConfigDict): Policy-specific configuration data. """ self.observation_space = observation_space self.action_space = action_space self.action_space_struct = get_base_struct_from_space(action_space) self.config = config if self.config.get("callbacks"): self.callbacks: "DefaultCallbacks" = self.config.get("callbacks")() else: from ray.rllib.agents.callbacks import DefaultCallbacks self.callbacks: "DefaultCallbacks" = DefaultCallbacks() # The global timestep, broadcast down from time to time from the # driver. self.global_timestep = 0 # The action distribution class to use for action sampling, if any. # Child classes may set this. self.dist_class = None # 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 self._model_init_state_automatically_added = False
[docs] @abstractmethod @DeveloperAPI def compute_actions( self, obs_batch: Union[List[TensorType], TensorType], state_batches: Optional[List[TensorType]] = None, prev_action_batch: Union[List[TensorType], TensorType] = None, prev_reward_batch: Union[List[TensorType], TensorType] = None, info_batch: Optional[Dict[str, list]] = None, episodes: Optional[List["MultiAgentEpisode"]] = 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 (Union[List[TensorType], TensorType]): Batch of observations. state_batches (Optional[List[TensorType]]): List of RNN state input batches, if any. prev_action_batch (Union[List[TensorType], TensorType]): Batch of previous action values. prev_reward_batch (Union[List[TensorType], TensorType]): Batch of previous rewards. info_batch (Optional[Dict[str, list]]): Batch of info objects. episodes (Optional[List[MultiAgentEpisode]] ): List of MultiAgentEpisode, 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 multiagent algorithms. explore (Optional[bool]): Whether to pick an exploitation or exploration action. Set to None (default) for using the value of `self.config["explore"]`. timestep (Optional[int]): The current (sampling) time step. Keyword Args: kwargs: forward compatibility placeholder Returns: Tuple: actions (TensorType): 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
[docs] @DeveloperAPI def compute_single_action( self, obs: TensorType, state: Optional[List[TensorType]] = None, prev_action: Optional[TensorType] = None, prev_reward: Optional[TensorType] = None, info: dict = None, episode: Optional["MultiAgentEpisode"] = None, clip_actions: bool = None, explore: Optional[bool] = None, timestep: Optional[int] = None, unsquash_actions: bool = None, **kwargs) -> \ Tuple[TensorType, List[TensorType], Dict[str, TensorType]]: """Unbatched version of compute_actions. Args: obs (TensorType): Single observation. state (Optional[List[TensorType]]): List of RNN state inputs, if any. prev_action (Optional[TensorType]): Previous action value, if any. prev_reward (Optional[TensorType]): Previous reward, if any. info (dict): Info object, if any. episode (Optional[MultiAgentEpisode]): this provides access to all of the internal episode state, which may be useful for model-based or multi-agent algorithms. unsquash_actions (bool): Should actions be unsquashed according to the Policy's action space? clip_actions (bool): Should actions be clipped according to the Policy's action space? explore (Optional[bool]): Whether to pick an exploitation or exploration action (default: None -> use self.config["explore"]). timestep (Optional[int]): The current (sampling) time step. Keyword Args: kwargs: Forward compatibility. Returns: Tuple: - actions (TensorType): Single action. - state_outs (List[TensorType]): List of RNN state outputs, if any. - info (dict): Dictionary of extra features, if any. """ # If policy works in normalized space, we should unsquash the action. # Use value of config.normalize_actions, if None. unsquash_actions = \ unsquash_actions if unsquash_actions is not None \ else self.config["normalize_actions"] clip_actions = clip_actions if clip_actions is not None else \ self.config["clip_actions"] prev_action_batch = None prev_reward_batch = None info_batch = None episodes = None state_batch = None if prev_action is not None: prev_action_batch = [prev_action] if prev_reward is not None: prev_reward_batch = [prev_reward] if info is not None: info_batch = [info] if episode is not None: episodes = [episode] if state is not None: state_batch = [ s.unsqueeze(0) if torch and isinstance(s, torch.Tensor) else np.expand_dims( s, 0) for s in state ] out = self.compute_actions( [obs], state_batch, prev_action_batch=prev_action_batch, prev_reward_batch=prev_reward_batch, info_batch=info_batch, 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] # If we work in normalized action space (normalize_actions=True), # we re-translate here into the env's action space. if unsquash_actions: single_action = unsquash_action(single_action, self.action_space_struct) # Clip, according to env's action space. elif clip_actions: single_action = clip_action(single_action, self.action_space_struct) # 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()}
[docs] @DeveloperAPI def compute_actions_from_input_dict( self, input_dict: SampleBatch, explore: bool = None, timestep: Optional[int] = None, episodes: Optional[List["MultiAgentEpisode"]] = None, **kwargs) -> \ Tuple[TensorType, List[TensorType], Dict[str, TensorType]]: """Computes actions from collected samples (across multiple-agents). Uses the currently "forward-pass-registered" samples from the collector to construct the input_dict for the Model. Args: input_dict (SampleBatch): A SampleBatch 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 (bool): Whether to pick an exploitation or exploration action (default: None -> use self.config["explore"]). timestep (Optional[int]): The current (sampling) time step. kwargs: forward compatibility placeholder Returns: Tuple: actions (TensorType): 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 (dict): 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, )
[docs] @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. Args: actions (Union[List[TensorType], TensorType]): Batch of actions, for which to retrieve the log-probs/likelihoods (given all other inputs: obs, states, ..). obs_batch (Union[List[TensorType], TensorType]): Batch of observations. state_batches (Optional[List[TensorType]]): List of RNN state input batches, if any. prev_action_batch (Optional[Union[List[TensorType], TensorType]]): Batch of previous action values. prev_reward_batch (Optional[Union[List[TensorType], TensorType]]): Batch of previous rewards. actions_normalized (bool): 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: TensorType: Batch of log probs/likelihoods, with shape: [BATCH_SIZE]. """ raise NotImplementedError
[docs] @DeveloperAPI def postprocess_trajectory( self, sample_batch: SampleBatch, other_agent_batches: Optional[Dict[AgentID, Tuple[ "Policy", SampleBatch]]] = None, episode: Optional["MultiAgentEpisode"] = 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. Args: sample_batch (SampleBatch): batch of experiences for the policy, which will contain at most one episode trajectory. other_agent_batches (dict): 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 (Optional[MultiAgentEpisode]): 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: SampleBatch: Postprocessed sample batch. """ return sample_batch
[docs] @DeveloperAPI def learn_on_batch(self, samples: SampleBatch) -> Dict[str, TensorType]: """Fused compute gradients and apply gradients call. Either this or the combination of compute/apply grads must be implemented by subclasses. Args: samples (SampleBatch): The SampleBatch object to learn from. Returns: Dict[str, TensorType]: Dictionary of extra metadata from compute_gradients(). Examples: >>> sample_batch = ev.sample() >>> ev.learn_on_batch(sample_batch) """ grads, grad_info = self.compute_gradients(samples) self.apply_gradients(grads) return grad_info
[docs] @DeveloperAPI def compute_gradients(self, postprocessed_batch: SampleBatch) -> \ Tuple[ModelGradients, Dict[str, TensorType]]: """Computes gradients against a batch of experiences. Either this or learn_on_batch() must be implemented by subclasses. Args: postprocessed_batch (SampleBatch): The SampleBatch object to use for calculating gradients. Returns: Tuple[ModelGradients, Dict[str, TensorType]]: - List of gradient output values. - Extra policy-specific info values. """ raise NotImplementedError
[docs] @DeveloperAPI def apply_gradients(self, gradients: ModelGradients) -> None: """Applies previously computed gradients. Either this or learn_on_batch() must be implemented by subclasses. Args: gradients (ModelGradients): The already calculated gradients to apply to this Policy. """ raise NotImplementedError
[docs] @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(). Returns: ModelWeights: Serializable copy or view of model weights. """ raise NotImplementedError
[docs] @DeveloperAPI def set_weights(self, weights: ModelWeights) -> None: """Sets this Policy's model's weights. Args: weights (ModelWeights): Serializable copy or view of model weights. """ raise NotImplementedError
[docs] @DeveloperAPI def get_exploration_state(self) -> Dict[str, TensorType]: """Returns the current exploration information of this policy. This information depends on the policy's Exploration object. Returns: Dict[str, TensorType]: Serializable information on the `self.exploration` object. """ return self.exploration.get_state()
@Deprecated(new="get_exploration_state", error=False) def get_exploration_info(self) -> Dict[str, TensorType]: return self.get_exploration_state()
[docs] @DeveloperAPI def is_recurrent(self) -> bool: """Whether this Policy holds a recurrent Model. Returns: bool: True if this Policy has-a RNN-based Model. """ return False
[docs] @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
[docs] @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 []
[docs] @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 devices. If the data is not evenly divisible by the batch size, excess data should be discarded. Args: batch (SampleBatch): The SampleBatch to load. buffer_index (int): The index of the buffer (a MultiGPUTowerStack) to use on the devices. Returns: int: The number of tuples loaded per device. """ raise NotImplementedError
[docs] @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: batch (SampleBatch): The SampleBatch to load. buffer_index (int): The index of the buffer (a MultiGPUTowerStack) to use on the devices. Returns: int: The number of tuples loaded per device. """ raise NotImplementedError
[docs] @DeveloperAPI def learn_on_loaded_batch(self, offset: int = 0, buffer_index: int = 0): """Runs a single step of SGD on 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 shared model weights based on the averaged per-device gradients. Args: offset (int): 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 (int): The index of the buffer (a MultiGPUTowerStack) to take the already pre-loaded data from. Returns: The outputs of extra_ops evaluated over the batch. """ raise NotImplementedError
[docs] @DeveloperAPI def get_state(self) -> Union[Dict[str, TensorType], List[TensorType]]: """Returns all local state. Note: Not to be confused with an RNN model's internal state. Returns: Union[Dict[str, TensorType], List[TensorType]]: Serialized local state. """ state = { # All the policy's weights. "weights": self.get_weights(), # The current global timestep. "global_timestep": self.global_timestep, } return state
[docs] @DeveloperAPI def set_state(self, state: object) -> None: """Restores all local state to the provided `state`. Args: state (object): 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"]
[docs] @DeveloperAPI def on_global_var_update(self, global_vars: Dict[str, TensorType]) -> None: """Called on an update to global vars. Args: global_vars (Dict[str, TensorType]): Global variables by str key, broadcast from the driver. """ # Store the current global time step (sum over all policies' sample # steps). self.global_timestep = global_vars["timestep"]
[docs] @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 (str): Local writable directory. onnx (int): If given, will export model in ONNX format. The value of this parameter set the ONNX OpSet version to use. """ raise NotImplementedError
[docs] @DeveloperAPI def import_model_from_h5(self, import_file: str) -> None: """Imports Policy from local file. Args: import_file (str): Local readable file. """ raise NotImplementedError
[docs] @DeveloperAPI def get_session(self) -> Optional["tf1.Session"]: """Returns tf.Session object to use for computing actions or None. Returns: Optional[tf1.Session]: The tf Session to use for computing actions and losses with this policy. """ return None
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.EPS_ID: ViewRequirement(), SampleBatch.UNROLL_ID: ViewRequirement(), SampleBatch.AGENT_INDEX: ViewRequirement(), "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 (bool): 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. """ 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) # 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.is_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. if self._loss is not None: self._loss(self, self.model, self.dist_class, train_batch) # Call the stats fn, if given. if stats_fn is not None: stats_fn(self, train_batch) # 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: self.view_requirements[key] = ViewRequirement() if 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]: 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] 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)) else: del self.view_requirements[key] 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 (int): 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(): if self.config["preprocessor_pref"] is not None and \ isinstance(view_req.space, (gym.spaces.Dict, gym.spaces.Tuple)): _, shape = ModelCatalog.get_action_shape( view_req.space, framework=self.config["framework"]) ret[view_col] = \ np.zeros((batch_size, ) + shape[1:], np.float32) 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) @Deprecated(new="save", error=False) def export_checkpoint(self, export_dir: str) -> None: """Export Policy checkpoint to local directory. Args: export_dir (str): Local writable directory. """ raise NotImplementedError