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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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 1 to create a new RNNSACConfig object from inherited base classes: SACConfig with example: obj = RNNSACConfig(algo_class)