minari 0.2.0

Datasets for offline deep reinforcement learning

Minari is the new name of this library. Minari used to be called Kabuki.

Minari is intended to be a Python library for conducting research in offline reinforcement learning, akin to an offline version of Gymnasium or an offline RL version of HuggingFace's datasets library. The goal is to release a fully working beta in late November or early December.

We have a public discord server (which we also use to coordinate development work) that you can join here: https://discord.gg/jfERDCSw.

Installation

pip install numpy cython

pip install git+https://github.com/Farama-Foundation/Minari.git

Downloading datasets

import minari

dataset = minari.download_dataset("LunarLander_v2_test-dataset")

Recreating Gymnasium environments (Coming very soon!)

import gymnasium as gym

env = gym.make(gym.SpecStack(json.loads(dataset.environment_stack)))

Uploading datasets

dataset.save(
    ".datasets/LunarLander-v2-test_dataset.hdf5"
)  # todo: abstract away parent directory and hdf5 extension
dataset = minari.upload_dataset("LunarLander_v2_test-dataset")

Saving to dataset format

It is not the aim of Minari to insist that you use a certain buffer implementation. However, in order to maintain standardisation across the library, we have a standardised format, the MinariDataset class, for saving replay buffers to file.

This converter will have tests to ensure formatting standards

Checking available remote datasets

import minari

minari.list_remote_datasets()

Checking available local datasets

import minari
minari.list_local_datasets()  # todo: implement

Datasets are stored in the .datasets directory in your project directory.

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Minari is a shortening of Minarai, the Japanese word for "learning by observation".