text: from ray.tune.registry import register_env
  from sagemaker_rl.ray_launcher import SageMakerRayLauncher
  
  
  class MyLauncher(SageMakerRayLauncher):
      """Battery optimization using Ray-RLLib on SageMaker.
  
      By default, PyTorch is used. However, TensorFlow can be chosen by setting SageMaker
      estimator's hyperparameter ``"rl.training.config.use_pytorch"`` to ``False``.
  
      See also: :class:`sagemaker_rl.ray_launcher.SageMakerRayLauncher`.
      """
  
      def register_env_creator(self):
          """Register a battery gym environment.
  
          See also: :meth:`~sagemaker_rl.ray_launcher.SageMakerRayLauncher.register_env_creator`.
          """
          from energy_storage_system.envs import SimpleBattery
  
          register_env("SimpleBattery-v1", lambda env_config: SimpleBattery(env_config))
  
      def get_experiment_config(self):
          """Get the default configuration, which will be overriden by hyperparameters.
  
          See also: :meth:`~sagemaker_rl.ray_launcher.SageMakerRayLauncher.get_experiment_config`.
          """
          return {
              "training": {
                  "env": "SimpleBattery-v1",
                  "run": "DQN",
                  "config": {
                      "use_pytorch": True,
                      "env_config": {
                          "MAX_STEPS_PER_EPISODE": 168,
                      },
                  },
                  "stop": {
                      "training_iteration": 5,
                  },
              }
          }
  
  
  if __name__ == "__main__":
      MyLauncher().train_main()
  