class_docstr: Defines a configuration class from which an RNNSAC can be built.
  
  Example:
      >>> config = RNNSACConfig().training(gamma=0.9, lr=0.01)        ...     .resources(num_gpus=0)        ...     .rollouts(num_rollout_workers=4)
      >>> print(config.to_dict())  # doctest: +SKIP
      >>> # Build a Algorithm object from the config and run 1 training iteration.
      >>> algo = config.build(env="CartPole-v1")
      >>> algo.train()  # doctest: +SKIP
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docstr: Sets the training related configuration.
  
  Args:
      zero_init_states: If True, assume a zero-initialized state input (no matter
          where in the episode the sequence is located).
          If False, store the initial states along with each SampleBatch, use
          it (as initial state when running through the network for training),
          and update that initial state during training (from the internal
          state outputs of the immediately preceding sequence).
  
  Returns:
      This updated AlgorithmConfig object.
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code: def training(
      self,
      *,
      zero_init_states: Optional[bool] = NotProvided,
      **kwargs,
  ) -> "RNNSACConfig":
      """Sets the training related configuration.
  
      Args:
          zero_init_states: If True, assume a zero-initialized state input (no matter
              where in the episode the sequence is located).
              If False, store the initial states along with each SampleBatch, use
              it (as initial state when running through the network for training),
              and update that initial state during training (from the internal
              state outputs of the immediately preceding sequence).
  
      Returns:
          This updated AlgorithmConfig object.
      """
      super().training(**kwargs)
      if zero_init_states is not NotProvided:
          self.zero_init_states = zero_init_states
  
      return self
  
file_path: ray/python/ray/rllib/algorithms/sac/rnnsac.py
imports: from typing import Type, Optional
  from ray.rllib.algorithms.sac import SAC, SACConfig
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.sac.rnnsac_torch_policy import RNNSACTorchPolicy
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE
label_desc: Use image node_id 2 for calling the RNNSACConfig obj's underlying member method code with example usage: obj.training() and returns: self