class_docstr: Defines a configuration class from which a MARWIL Algorithm can be built.
  
  
  Example:
      >>> from ray.rllib.algorithms.marwil import MARWILConfig
      >>> # Run this from the ray directory root.
      >>> config = MARWILConfig()  # doctest: +SKIP
      >>> config = config.training(beta=1.0, lr=0.00001, gamma=0.99)  # doctest: +SKIP
      >>> config = config.offline_data(  # doctest: +SKIP
      ...     input_=["./rllib/tests/data/cartpole/large.json"])
      >>> print(config.to_dict()) # doctest: +SKIP
      ...
      >>> # Build an Algorithm object from the config and run 1 training iteration.
      >>> algo = config.build()  # doctest: +SKIP
      >>> algo.train() # doctest: +SKIP
  
  Example:
      >>> from ray.rllib.algorithms.marwil import MARWILConfig
      >>> from ray import tune
      >>> config = MARWILConfig()
      >>> # Print out some default values.
      >>> print(config.beta)  # doctest: +SKIP
      >>> # Update the config object.
      >>> config.training(lr=tune.grid_search(  # doctest: +SKIP
      ...     [0.001, 0.0001]), beta=0.75)
      >>> # Set the config object's data path.
      >>> # Run this from the ray directory root.
      >>> config.offline_data( # doctest: +SKIP
      ...     input_=["./rllib/tests/data/cartpole/large.json"])
      >>> # Set the config object's env, used for evaluation.
      >>> 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
      ...     "MARWIL",
      ...     param_space=config.to_dict(),
      ... ).fit()
docstr: Initializes a MARWILConfig instance.
docstr_tok: ["Initializes","a","MARWILConfig","instance","."]
imports: from typing import Callable, Optional, Type, Union
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.execution.rollout_ops import synchronous_parallel_sample
  from ray.rllib.execution.train_ops import multi_gpu_train_one_step, train_one_step
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import deprecation_warning
  from ray.rllib.utils.metrics import NUM_AGENT_STEPS_SAMPLED, NUM_ENV_STEPS_SAMPLED, SYNCH_WORKER_WEIGHTS_TIMER, SAMPLE_TIMER
  from ray.rllib.utils.typing import EnvType, ResultDict
  from ray.tune.logger import Logger
label_desc: Use image node_id 1 to create a new MARWILConfig object from inherited base classes: AlgorithmConfig with example: obj = MARWILConfig(algo_class)