text: from abc import ABCMeta, abstractmethod
  import gym
  from gym.spaces import Box
  import logging
  import numpy as np
  import tree
  from typing import Dict, List, Optional
  
  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 DeveloperAPI
  from ray.rllib.utils.exploration.exploration import Exploration
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.from_config import from_config
  from ray.rllib.utils.spaces.space_utils import get_base_struct_from_space, \
      unbatch
  from ray.rllib.utils.tracking_dict import UsageTrackingDict
  from ray.rllib.utils.typing import AgentID, ModelGradients, ModelWeights, \
      TensorType, TrainerConfigDict, Tuple, Union
  
  torch, _ = try_import_torch()
  
  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"
  
  
  @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
  
      @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, with shape like [STATE_SIZE, BATCH_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_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 = False,
              explore: Optional[bool] = None,
              timestep: Optional[int] = 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.
              clip_actions (bool): Should actions be clipped?
              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.
          """
          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 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()}
  
      @DeveloperAPI
      def compute_actions_from_input_dict(
              self,
              input_dict: Dict[str, TensorType],
              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).
  
          Note: This is an experimental API method.
  
          Only used so far by the Sampler iff `_use_trajectory_view_api=True`
          (also only supported for torch).
          Uses the currently "forward-pass-registered" samples from the collector
          to construct the input_dict for the Model.
  
          Args:
              input_dict (Dict[str, TensorType]): An input dict mapping str
                  keys to Tensors. `input_dict` already abides to the Policy's
                  as well as the Model's view requirements and can 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, with shape like [STATE_SIZE, BATCH_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 = [
              # TODO: (sven) remove unsqueezing code here for non-traj.view API.
              s if self.config.get("_use_trajectory_view_api", False) else
              s.unsqueeze(0)
              if torch and isinstance(s, torch.Tensor) else np.expand_dims(s, 0)
              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,
          )
  
      @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) -> 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.
  
          Returns:
              TensorType: Batch of log probs/likelihoods, with shape:
                  [BATCH_SIZE].
          """
          raise NotImplementedError
  
      @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
  
      @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
  
      @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
  
      @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
  
      @DeveloperAPI
      def get_weights(self) -> ModelWeights:
          """Returns model weights.
  
          Returns:
              ModelWeights: Serializable copy or view of model weights.
          """
          raise NotImplementedError
  
      @DeveloperAPI
      def set_weights(self, weights: ModelWeights) -> None:
          """Sets model weights.
  
          Args:
              weights (ModelWeights): Serializable copy or view of model weights.
          """
          raise NotImplementedError
  
      @DeveloperAPI
      def get_exploration_info(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_info()
  
      @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
  
      @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) -> Union[Dict[str, TensorType], List[TensorType]]:
          """Saves all local state.
  
          Returns:
              Union[Dict[str, TensorType], List[TensorType]]: Serialized local
                  state.
          """
          return self.get_weights()
  
      @DeveloperAPI
      def set_state(self, state: object) -> None:
          """Restores all local state.
  
          Args:
              state (obj): Serialized local state.
          """
          self.set_weights(state)
  
      @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"]
  
      @DeveloperAPI
      def export_model(self, export_dir: str) -> None:
          """Export Policy to local directory for serving.
  
          Args:
              export_dir (str): Local writable directory.
          """
          raise NotImplementedError
  
      @DeveloperAPI
      def export_checkpoint(self, export_dir: str) -> None:
          """Export Policy checkpoint to local directory.
  
          Args:
              export_dir (str): Local writable directory.
          """
          raise NotImplementedError
  
      @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
  
      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),
              # 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)
          input_dict = self._lazy_tensor_dict(self._dummy_batch)
          actions, state_outs, extra_outs = \
              self.compute_actions_from_input_dict(input_dict, 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] = np.zeros_like(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))
          batch_for_postproc = UsageTrackingDict(self._dummy_batch)
          batch_for_postproc.count = self._dummy_batch.count
          self.exploration.postprocess_trajectory(self, batch_for_postproc)
          postprocessed_batch = self.postprocess_trajectory(batch_for_postproc)
          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)
          # Wrap `train_batch` with a to-tensor UsageTrackingDict.
          train_batch = self._lazy_tensor_dict(postprocessed_batch)
          if seq_lens is not None:
              train_batch["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 self.config["_use_trajectory_view_api"] and \
                  auto_remove_unneeded_view_reqs:
              # Add those needed for postprocessing and training.
              all_accessed_keys = train_batch.accessed_keys | \
                                  batch_for_postproc.accessed_keys | \
                                  batch_for_postproc.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.
                  for key in batch_for_postproc.accessed_keys:
                      if key not in train_batch.accessed_keys and \
                              key in self.view_requirements and \
                              key not in self.model.view_requirements:
                          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 and REWARDS, 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] 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 batch_for_postproc.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 isinstance(view_req.space, (gym.spaces.Dict, gym.spaces.Tuple)):
                  _, shape = ModelCatalog.get_action_shape(view_req.space)
                  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] = np.zeros_like([[
                          view_req.space.sample()
                          for _ in range(view_req.shift_to -
                                         view_req.shift_from + 1)
                      ] for _ in range(batch_size)])
                  # Set of (probably non-consecutive) indices.
                  elif isinstance(view_req.shift, (list, tuple)):
                      ret[view_col] = np.zeros_like([[
                          view_req.space.sample()
                          for t in range(len(view_req.shift))
                      ] for _ in range(batch_size)])
                  # Single shift int value.
                  else:
                      if isinstance(view_req.space, gym.spaces.Space):
                          ret[view_col] = np.zeros_like([
                              view_req.space.sample() for _ in range(batch_size)
                          ])
                      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, _dont_check_lens=True)
  
      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
          # Add state-ins to this model's view.
          for i, state in enumerate(obj.get_initial_state()):
              space = Box(-1.0, 1.0, shape=state.shape) if \
                  hasattr(state, "shape") else state
              view_reqs = model.view_requirements if model else \
                  self.view_requirements
              view_reqs["state_in_{}".format(i)] = ViewRequirement(
                  "state_out_{}".format(i),
                  shift=-1,
                  batch_repeat_value=self.config.get("model", {}).get(
                      "max_seq_len", 1),
                  space=space)
              view_reqs["state_out_{}".format(i)] = ViewRequirement(
                  space=space, used_for_training=True)
  
  
  def clip_action(action, action_space):
      """Clips all actions in `flat_actions` according to the given Spaces.
  
      Args:
          flat_actions (List[np.ndarray]): The (flattened) list of single action
              components. List will have len=1 for "primitive" action Spaces.
          flat_space (List[Space]): The (flattened) list of single action Space
              objects. Has to be of same length as `flat_actions`.
  
      Returns:
          List[np.ndarray]: Flattened list of single clipped "primitive" actions.
      """
  
      def map_(a, s):
          if isinstance(s, gym.spaces.Box):
              a = np.clip(a, s.low, s.high)
          return a
  
      return tree.map_structure(map_, action, action_space)
  