class_docstr: Defines a configuration class from which a A3C Algorithm can be built.
  
  Example:
      >>> from ray import tune
      >>> from ray.rllib.algorithms.a3c import A3CConfig
      >>> config = A3CConfig() # doctest: +SKIP
      >>> config = config.training(lr=0.01, grad_clip=30.0) # doctest: +SKIP
      >>> config = config.resources(num_gpus=0) # doctest: +SKIP
      >>> config = config.rollouts(num_rollout_workers=4) # doctest: +SKIP
      >>> config = config.environment("CartPole-v1") # doctest: +SKIP
      >>> print(config.to_dict())  # doctest: +SKIP
      >>> # Build a Algorithm object from the config and run 1 training iteration.
      >>> algo = config.build()  # doctest: +SKIP
      >>> algo.train()  # doctest: +SKIP
  
  Example:
      >>> from ray.rllib.algorithms.a3c import A3CConfig
      >>> config = A3CConfig()
      >>> # Print out some default values.
      >>> print(config.sample_async)  # doctest: +SKIP
      >>> # Update the config object.
      >>> config = config.training( # doctest: +SKIP
      ...     lr=tune.grid_search([0.001, 0.0001]), use_critic=False)
      >>> # 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
      ...     "A3C",
      ...     stop={"episode_reward_mean": 200},
      ...     param_space=config.to_dict(),
      ... ).fit()
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docstr: Initializes a A3CConfig instance.
docstr_tok: ["Initializes","a","A3CConfig","instance","."]
imports: import logging
  from typing import Any, Dict, List, Optional, Type, Union
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.metrics import APPLY_GRADS_TIMER, GRAD_WAIT_TIMER, NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED, SYNCH_WORKER_WEIGHTS_TIMER
  from ray.rllib.utils.metrics.learner_info import LearnerInfoBuilder
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 1 to create a new A3CConfig object from inherited base classes: AlgorithmConfig with example: obj = A3CConfig(algo_class)