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
class_docstr_tok: ["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"]
code: def validate(self) -> None:
      # Call super's validation method.
      super().validate()
  
      # Add the `burn_in` to the Model's max_seq_len.
      replay_sequence_length = (
          self.replay_buffer_config["replay_burn_in"]
          + self.model["max_seq_len"]
      )
      # Check if user tries to set replay_sequence_length (to anything
      # other than the proper value)
      if self.replay_buffer_config.get(
          "replay_sequence_length", None
      ) not in [
          None,
          -1,
          replay_sequence_length,
      ]:
          raise ValueError(
              "`replay_sequence_length` is calculated automatically to be "
              "config.model['max_seq_len'] + config['replay_burn_in']. Leave "
              "config.replay_buffer_config['replay_sequence_length'] blank to avoid "
              "this error."
          )
      # Set the replay sequence length to the max_seq_len of the model.
      self.replay_buffer_config[
          "replay_sequence_length"
      ] = replay_sequence_length
  
      if self.framework_str != "torch":
          raise ValueError(
              "Only `framework=torch` supported so far for RNNSAC!"
          )
  
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 3 for calling the RNNSACConfig obj's underlying member method code with example usage: obj.validate() without return types