class_docstr: Implements an exploration strategy for Policies.
  
  An Exploration takes model outputs, a distribution, and a timestep from
  the agent and computes an action to apply to the environment using an
  implemented exploration schema.
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docstr: Sets the Exploration object's state to the given values.
  
  Note that some exploration components are stateless, even though they
  decay some values over time (e.g. EpsilonGreedy). However the decay is
  only dependent on the current global timestep of the policy and we
  therefore don't need to keep track of it.
  
  Args:
      state: The state to set this Exploration to.
      sess: An optional tf Session object to use.
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code: def set_state(
      self, state: object, sess: Optional["tf.Session"] = None
  ) -> None:
      """Sets the Exploration object's state to the given values.
  
      Note that some exploration components are stateless, even though they
      decay some values over time (e.g. EpsilonGreedy). However the decay is
      only dependent on the current global timestep of the policy and we
      therefore don't need to keep track of it.
  
      Args:
          state: The state to set this Exploration to.
          sess: An optional tf Session object to use.
      """
      pass
  
file_path: ray/rllib/utils/exploration/exploration.py
imports: from gymnasium.spaces import Space
  from typing import Dict, List, Optional, Union, TYPE_CHECKING
  from ray.rllib.env.base_env import BaseEnv
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import DeveloperAPI, PublicAPI
  from ray.rllib.utils.framework import try_import_torch, TensorType
  from ray.rllib.utils.typing import LocalOptimizer, AlgorithmConfigDict
label_desc: Use image node_id 9 for calling the Exploration obj's underlying member method code with example usage: obj.set_state(state, sess) without return types