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: Sets the training related configuration.
  
  Args:
      use_gae: If true, use the Generalized Advantage Estimator (GAE)
          with a value function, see https://arxiv.org/pdf/1506.02438.pdf.
      lambda_: The GAE (lambda) parameter.
      kl_coeff: Initial coefficient for KL divergence.
      vf_loss_coeff: Coefficient of the value function loss.
      entropy_coeff: Coefficient of the entropy regularizer.
      clip_param: PPO clip parameter.
      vf_clip_param: Clip param for the value function. Note that this is
          sensitive to the scale of the rewards. If your expected V is large,
          increase this.
      grad_clip: If specified, clip the global norm of gradients by this amount.
      kl_target: Target value for KL divergence.
      inner_adaptation_steps: Number of Inner adaptation steps for the MAML
          algorithm.
      maml_optimizer_steps: Number of MAML steps per meta-update iteration
          (PPO steps).
      inner_lr: Inner Adaptation Step size.
      use_meta_env: Use Meta Env Template.
  
  Returns:
      This updated AlgorithmConfig object.
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code: def training(
      self,
      *,
      use_gae: Optional[bool] = NotProvided,
      lambda_: Optional[float] = NotProvided,
      kl_coeff: Optional[float] = NotProvided,
      vf_loss_coeff: Optional[float] = NotProvided,
      entropy_coeff: Optional[float] = NotProvided,
      clip_param: Optional[float] = NotProvided,
      vf_clip_param: Optional[float] = NotProvided,
      grad_clip: Optional[float] = NotProvided,
      kl_target: Optional[float] = NotProvided,
      inner_adaptation_steps: Optional[int] = NotProvided,
      maml_optimizer_steps: Optional[int] = NotProvided,
      inner_lr: Optional[float] = NotProvided,
      use_meta_env: Optional[bool] = NotProvided,
      **kwargs,
  ) -> "MAMLConfig":
      """Sets the training related configuration.
  
      Args:
          use_gae: If true, use the Generalized Advantage Estimator (GAE)
              with a value function, see https://arxiv.org/pdf/1506.02438.pdf.
          lambda_: The GAE (lambda) parameter.
          kl_coeff: Initial coefficient for KL divergence.
          vf_loss_coeff: Coefficient of the value function loss.
          entropy_coeff: Coefficient of the entropy regularizer.
          clip_param: PPO clip parameter.
          vf_clip_param: Clip param for the value function. Note that this is
              sensitive to the scale of the rewards. If your expected V is large,
              increase this.
          grad_clip: If specified, clip the global norm of gradients by this amount.
          kl_target: Target value for KL divergence.
          inner_adaptation_steps: Number of Inner adaptation steps for the MAML
              algorithm.
          maml_optimizer_steps: Number of MAML steps per meta-update iteration
              (PPO steps).
          inner_lr: Inner Adaptation Step size.
          use_meta_env: Use Meta Env Template.
  
      Returns:
          This updated AlgorithmConfig object.
      """
      # Pass kwargs onto super's `training()` method.
      super().training(**kwargs)
  
      if use_gae is not NotProvided:
          self.use_gae = use_gae
      if lambda_ is not NotProvided:
          self.lambda_ = lambda_
      if kl_coeff is not NotProvided:
          self.kl_coeff = kl_coeff
      if vf_loss_coeff is not NotProvided:
          self.vf_loss_coeff = vf_loss_coeff
      if entropy_coeff is not NotProvided:
          self.entropy_coeff = entropy_coeff
      if clip_param is not NotProvided:
          self.clip_param = clip_param
      if vf_clip_param is not NotProvided:
          self.vf_clip_param = vf_clip_param
      if grad_clip is not NotProvided:
          self.grad_clip = grad_clip
      if kl_target is not NotProvided:
          self.kl_target = kl_target
      if inner_adaptation_steps is not NotProvided:
          self.inner_adaptation_steps = inner_adaptation_steps
      if maml_optimizer_steps is not NotProvided:
          self.maml_optimizer_steps = maml_optimizer_steps
      if inner_lr is not NotProvided:
          self.inner_lr = inner_lr
      if use_meta_env is not NotProvided:
          self.use_meta_env = use_meta_env
  
      return self
  
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 2 for calling the MAMLConfig obj's underlying member method code with example usage: obj.training() and returns: self