eta_ctrl.core_utils module

eta_ctrl.core_utils.vectorize_environment(env: type[BaseEnv], config_run: ConfigRun, env_settings: EnvSettings, callback: Callable[[BaseEnv], None], verbose: int = 2, vectorizer: type[DummyVecEnv | SubprocVecEnv] | None = None, n: int = 1, seed: int | None = None, *, training: bool = False, monitor_wrapper: bool = False, norm_wrapper_obs: bool = False, norm_wrapper_reward: bool = False) → VecNormalize | VecEnv[source]

Vectorize the environment and automatically apply normalization wrappers if configured.

Parameters:

  • env – Environment class which will be instantiated and vectorized.

  • config_run – Configuration for a specific optimization run.

  • env_settings – Configuration settings dictionary for the environment which is being initialized.

  • callback – Callback to call with an environment instance.

  • verbose – Logging verbosity to use in the environment.

  • vectorizer – Vectorizer class to use for vectorizing the environments.

  • n – Number of vectorized environments to create.

  • training – Flag to identify whether the environment should be initialized for training or playing. If true, it will be initialized for training.

  • norm_wrapper_obs – Flag to determine whether observations from the environments should be normalized.

  • norm_wrapper_reward – Flag to determine whether rewards from the environments should be normalized.

Returns:

Vectorized environments, possibly also wrapped in a normalizer.

eta_ctrl.core_utils.initialize_model(algo: type[BaseAlgorithm], policy: type[BasePolicy], envs: VecEnv | VecNormalize, algo_settings: AlgoSettings, seed: int | None = None, *, tensorboard_log: bool = False, log_path: Path | None = None) → BaseAlgorithm[source]

Initialize a new model or algorithm.

Parameters:

  • algo – Algorithm to initialize.

  • policy – The policy that should be used by the algorithm.

  • envs – The environment which the algorithm operates on.

  • algo_settings – Additional settings for the algorithm.

  • seed – Random seed to be used by the algorithm.

  • tensorboard_log – Flag to enable logging to tensorboard.

  • log_path – Path for tensorboard log. Online required if logging is true

Returns:

Initialized model.

eta_ctrl.core_utils.load_model(algo: type[BaseAlgorithm], envs: VecEnv | VecNormalize, algo_settings: AlgoSettings, model_path: Path, *, tensorboard_log: bool = False, log_path: Path | None = None) → BaseAlgorithm[source]

Load an existing model.

Parameters:

  • algo – Algorithm type of the model to be loaded.

  • envs – The environment which the algorithm operates on.

  • algo_settings – Additional settings for the algorithm.

  • model_path – Path to load the model from.

  • tensorboard_log – Flag to enable logging to tensorboard.

  • log_path – Path for tensorboard log. Online required if logging is true

Returns:

Initialized model.