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. This library is currently in beta, and we're targeting having a complete initial release in February
We have a public discord server (which we also use to coordinate development work) that you can join here: https://discord.gg/jfERDCSw.
pip install numpy cython
pip install git+https://github.com/Farama-Foundation/Minari.git
import minari
dataset = minari.download_dataset("LunarLander_v2_remote-test-dataset")
import json
import gymnasium as gym
from gymnasium.utils.serialize_spec_stack import deserialise_spec_stack
env = gym.make(deserialise_spec_stack(json.loads(dataset.environment_stack)))
dataset.save()
dataset = minari.upload_dataset("LunarLander_v2_remote-test-dataset")
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
import minari
minari.list_remote_datasets()
import minari
minari.list_local_datasets()
Minari is a shortening of Minarai, the Japanese word for "learning by observation".