lit_sb3.utils.model

source module lit_sb3.utils.model

Trained model path and hyperparameter loading utilities.

Functions

• get_saved_hyperparams — Load hyperparameters saved by :class:~lit_sb3.core.exp_manager.ExperimentManager.

• get_hf_trained_models — Fetch trained models from a HuggingFace organisation.

• get_trained_models — Scan log_folder for trained agents saved by the ExperimentManager.

• get_model_path — Retrieve the path to a trained model, along with its log directory.

source get_saved_hyperparams(stats_path: str | Path, norm_reward: bool = False, test_mode: bool = False) → tuple[dict[str, Any], str | Path | None]

Load hyperparameters saved by :class:~lit_sb3.core.exp_manager.ExperimentManager.

Parameters

• stats_path : str | Path — Path to the folder containing config.yml.

• norm_reward : bool — Whether to keep reward normalisation active.

• test_mode : bool — Disable reward normalisation and training flag.

Returns

• tuple[dict[str, Any], str | Path | None] — Tuple of (hyperparams_dict, stats_path_or_None).

source get_hf_trained_models(organization: str = 'sb3') → dict[str, tuple[str, str]]

Fetch trained models from a HuggingFace organisation.

Returns a dict mapping model names to (algo, env_id) tuples, mirroring :func:get_trained_models.

Requires huggingface_hub to be installed.

:param organization: HuggingFace organisation to search. :return: Dict mapping model names to (algo, env_id) tuples.

Raises

• ImportError

source get_trained_models(log_folder: str) → dict[str, tuple[str, str]]

Scan log_folder for trained agents saved by the ExperimentManager.

Parameters

• log_folder : str — Root log directory (same as --log-folder in training).

Returns

• dict[str, tuple[str, str]] — Dict mapping ModelName strings to (algo, env_id) tuples.

source get_model_path(exp_id: int, folder: str, algo: str, env_name: EnvironmentName, load_best: bool = False, load_checkpoint: str | None = None, load_last_checkpoint: bool = False) → tuple[str, str, str]

Retrieve the path to a trained model, along with its log directory.

Parameters

• exp_id : int — Experiment ID (0 → use the latest available run).

• folder : str — Root folder where run directories are stored (e.g. logs/).

• algo : str — Algorithm name, e.g. "ppo".

• env_name : EnvironmentName — :class:EnvironmentName wrapper for the target environment.

• load_best : bool — When True, load best_model.zip instead of the final checkpoint.

• load_checkpoint : str | None — If non-None, load the model saved at this specific step count (e.g. "10000").

• load_last_checkpoint : bool — When True, load the numerically largest step-count checkpoint rather than the final model.

Returns

• tuple[str, str, str] — (name_prefix, model_path, log_path) where name_prefix is a human-readable identifier, model_path points to the .zip file, and log_path is the run directory.

Raises

• ValueError