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.
class_docstr_tok: ["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","."]
docstr: Returns the current exploration state.
  
  Args:
      sess: An optional tf Session object to use.
  
  Returns:
      The Exploration object's current state.
docstr_tok: ["Returns","the","current","exploration","state",".","Args",":","sess",":","An","optional","tf","Session","object","to","use",".","Returns",":","The","Exploration","object","'s","current","state","."]
code: def get_state(
      self, sess: Optional["tf.Session"] = None
  ) -> Dict[str, TensorType]:
      """Returns the current exploration state.
  
      Args:
          sess: An optional tf Session object to use.
  
      Returns:
          The Exploration object's current state.
      """
      return {}
  
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 8 for calling the Exploration obj's underlying member method code with example usage: obj.get_state(sess) and returns: dict