class_docstr: Defines a configuration class from which an ES Algorithm can be built.
  
  Example:
      >>> from ray.rllib.algorithms.es import ESConfig
      >>> config = ESConfig()  # doctest: +SKIP
      >>> config = config.training(sgd_stepsize=0.02, report_length=20)#doctest: +SKIP
      >>> config = config.resources(num_gpus=0)  # doctest: +SKIP
      >>> config = config.rollouts(num_rollout_workers=4)  # doctest: +SKIP
      >>> print(config.to_dict())  # doctest: +SKIP
      >>> # Build a Algorithm object from the config and run 1 training iteration.
      >>> algo = config.build(env="CartPole-v1")  # doctest: +SKIP
      >>> algo.train()  # doctest: +SKIP
  
  Example:
      >>> from ray.rllib.algorithms.es import ESConfig
      >>> from ray import tune
      >>> config = ESConfig()
      >>> # Print out some default values.
      >>> print(config.action_noise_std)  # doctest: +SKIP
      >>> # Update the config object.
      >>> config = config.training(  # doctest: +SKIP
      ...     rollouts_used=tune.grid_search([32, 64]), eval_prob=0.5)
      >>> # Set the config object's env.
      >>> config = config.environment(env="CartPole-v1")  # doctest: +SKIP
      >>> # Use to_dict() to get the old-style python config dict
      >>> # when running with tune.
      >>> tune.Tuner(  # doctest: +SKIP
      ...     "ES",
      ...     run_config=ray.air.RunConfig(stop={"episode_reward_mean": 200}),
      ...     param_space=config.to_dict(),
      ... ).fit()
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docstr: Initializes a ESConfig instance.
docstr_tok: ["Initializes","a","ESConfig","instance","."]
imports: import logging
  import random
  import time
  from collections import namedtuple
  from typing import Dict, List, Optional
  import numpy
  from rllib_es.es import optimizers, utils
  from rllib_es.es.es_tf_policy import ESTFPolicy, rollout
  import ray
  from ray.rllib.algorithms import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.env.env_context import EnvContext
  from ray.rllib.evaluation.worker_set import WorkerSet
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
  from ray.rllib.utils import FilterManager
  from ray.rllib.utils.actor_manager import FaultAwareApply
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import Deprecated
  from ray.rllib.utils.metrics import NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED
  from ray.rllib.utils.torch_utils import set_torch_seed
  from ray.rllib.utils.typing import PolicyID
label_desc: Use image node_id 1 to create a new ESConfig object from inherited base classes: AlgorithmConfig with example: obj = ESConfig()