Minari#

Create Minari Dataset#

minari.create_dataset_from_collector_env(dataset_id: str, collector_env: DataCollectorV0, algorithm_name: str | None = None, author: str | None = None, author_email: str | None = None, code_permalink: str | None = None)[source]#

Create a Minari dataset using the data collected from stepping with a Gymnasium environment wrapped with a DataCollectorV0 Minari wrapper.

The dataset_id parameter corresponds to the name of the dataset, with the syntax as follows: (env_name-)(dataset_name)(-v(version)) where env_name identifies the name of the environment used to generate the dataset dataset_name. This dataset_id is used to load the Minari datasets with minari.load_dataset().

Parameters:

  • dataset_id (str) – name id to identify Minari dataset

  • collector_env (DataCollectorV0) – Gymnasium environment used to collect the buffer data

  • buffer (list[Dict[str, Union[list, Dict]]]) – list of episode dictionaries with data

  • algorithm_name (Optional[str], optional) – name of the algorithm used to collect the data. Defaults to None.

  • author (Optional[str], optional) – author that generated the dataset. Defaults to None.

  • author_email (Optional[str], optional) – email of the author that generated the dataset. Defaults to None.

  • code_permalink (Optional[str], optional) – link to relevant code used to generate the dataset. Defaults to None.

Returns:

MinariDataset

minari.create_dataset_from_buffers(dataset_id: str, env: Env, buffer: List[Dict[str, list | Dict]], algorithm_name: str | None = None, author: str | None = None, author_email: str | None = None, code_permalink: str | None = None)[source]#

Create Minari dataset from a list of episode dictionary buffers.

The dataset_id parameter corresponds to the name of the dataset, with the syntax as follows: (env_name-)(dataset_name)(-v(version)) where env_name identifies the name of the environment used to generate the dataset dataset_name. This dataset_id is used to load the Minari datasets with minari.load_dataset().

Each episode dictionary buffer must have the following items:

  • observations: np.ndarray of step observations. shape = (total_episode_steps + 1, (observation_shape)). Should include initial and final observation

  • actions: np.ndarray of step action. shape = (total_episode_steps + 1, (action_shape)).

  • rewards: np.ndarray of step rewards. shape = (total_episode_steps + 1, 1).

  • terminations: np.ndarray of step terminations. shape = (total_episode_steps + 1, 1).

  • truncations: np.ndarray of step truncations. shape = (total_episode_steps + 1, 1).

Other additional items can be added as long as the values are np.ndarray’s or other nested dictionaries.

Parameters:

  • dataset_id (str) – name id to identify Minari dataset

  • env (gym.Env) – Gymnasium environment used to collect the buffer data

  • buffer (list[Dict[str, Union[list, Dict]]]) – list of episode dictionaries with data

  • algorithm_name (Optional[str], optional) – name of the algorithm used to collect the data. Defaults to None.

  • author (Optional[str], optional) – author that generated the dataset. Defaults to None.

  • author_email (Optional[str], optional) – email of the author that generated the dataset. Defaults to None.

  • code_permalink (Optional[str], optional) – link to relevant code used to generate the dataset. Defaults to None.

Returns:

MinariDataset

Load Minari Dataset#

minari.load_dataset(dataset_id: str)[source]#

Retrieve Minari dataset from local database.

Parameters:

dataset_id (str) – name id of Minari dataset

Returns:

MinariDataset

Split Minari Dataset#

minari.split_dataset(dataset: MinariDataset, sizes: List[int], seed: int | None = None) → List[MinariDataset][source]#

Split a MinariDataset in multiple datasets.

Parameters:

  • dataset (MinariDataset) – the MinariDataset to split

  • sizes (List[int]) – sizes of the resulting datasets

  • seed (Optiona[int]) – random seed

Returns:

datasets (List[MinariDataset]) – resulting list of datasets

Download Minari Dataset#

minari.download_dataset(dataset_id: str)[source]#

Download dataset from remote Farama server.

Parameters:

dataset_id (str) – name id of the Minari dataset

List Minari Datasets#

minari.list_local_datasets() → Dict[str, Dict[str, str | int | bool]][source]#

Get the ids and metadata of all the Minari datasets in the local database.

Returns:

Dict[str, Dict[str, str]] – keys the names of the Minari datasets and values the metadata

minari.list_remote_datasets() → Dict[str, Dict[str, str]][source]#

Get the names and metadata of all the Minari dataset in the remote Farama server.

Returns:

Dict[str, Dict[str, str]] – keys the names of the Minari datasets and values the metadata

Delete Minari Datasets#

minari.delete_dataset(dataset_id: str)[source]#

Delete a Minari dataset from the local Minari database.

Parameters:

dataset_id (str) – name id of the Minari dataset

Combine Minari Datasets#

minari.combine_datasets(datasets_to_combine: List[MinariDataset], new_dataset_id: str)[source]#

Combine a group of MinariDataset in to a single dataset with its own name id.

A new HDF5 metadata attribute will be added to the new dataset called combined_datasets. This will contain a list of strings with the dataset names that were combined to form this new Minari dataset.

Parameters:

  • datasets_to_combine (list[MinariDataset]) – list of datasets to be combined

  • new_dataset_id (str) – name id for the newly created dataset