class_docstr: Recurrent exponentially Q-weighted policy.
class_docstr_tok: ["Recurrent","exponentially","Q-weighted","policy","."]
code: def __init__(
      self,
      policy_network: snt.Module,
      critic_network: snt.Module,
      temperature_beta: float = 1.0,
      num_action_samples: int = 16,
  ):
      super().__init__(name="RecurrentExpQWeightedPolicy")
      self._policy_network = policy_network
      self._critic_network = critic_network
      self._num_action_samples = num_action_samples
      self._temperature_beta = temperature_beta
  
imports: import functools
  from typing import NamedTuple, Optional, Sequence, Tuple
  from absl import logging
  from acme import types
  from acme.tf import savers
  from acme.tf import utils
  from acme.tf.networks import base
  import sonnet
  import tensorflow
  import tensorflow_probability
  import tree
label_desc: Use image node_id 1 to create a new RecurrentExpQWeightedPolicy object from inherited base classes: snt with example: obj = RecurrentExpQWeightedPolicy(policy_network, critic_network, temperature_beta, num_action_samples)