class_docstr: Defines a configuration class from which a MAML Algorithm can be built.
  
  Example:
      >>> from ray.rllib.algorithms.maml import MAMLConfig
      >>> config = MAMLConfig().training(use_gae=False).resources(num_gpus=1)
      >>> 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.maml import MAMLConfig
      >>> from ray import air
      >>> from ray import tune
      >>> config = MAMLConfig()
      >>> # Print out some default values.
      >>> print(config.lr)  # doctest: +SKIP
      >>> # Update the config object.
      >>> config = config.training(  # doctest: +SKIP
      ...     grad_clip=tune.grid_search([10.0, 40.0]))
      >>> # Set the config object's env.
      >>> config = config.environment(env="CartPole-v1")
      >>> # Use to_dict() to get the old-style python config dict
      >>> # when running with tune.
      >>> tune.Tuner(  # doctest: +SKIP
      ...     "MAML",
      ...     run_config=air.RunConfig(stop={"episode_reward_mean": 200}),
      ...     param_space=config.to_dict(),
      ... ).fit()
docstr: Initializes a PGConfig instance.
docstr_tok: ["Initializes","a","PGConfig","instance","."]
imports: import logging
  from typing import Optional, Type
  import numpy
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.evaluation.metrics import collect_metrics, get_learner_stats
  from ray.rllib.evaluation.worker_set import WorkerSet
  from ray.rllib.execution.common import STEPS_SAMPLED_COUNTER, STEPS_TRAINED_COUNTER, STEPS_TRAINED_THIS_ITER_COUNTER, _get_shared_metrics
  from ray.rllib.execution.metric_ops import CollectMetrics
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import concat_samples, convert_ma_batch_to_sample_batch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE
  from ray.rllib.utils.metrics.learner_info import LEARNER_INFO
  from ray.rllib.utils.sgd import standardized
  from ray.util.iter import LocalIterator, from_actors
label_desc: Use image node_id 1 to create a new MAMLConfig object from inherited base classes: AlgorithmConfig with example: obj = MAMLConfig(algo_class)