Datasets for offline deep reinforcement learning
Project description
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.
Minari is a shortening of Minarai, the Japanese word for "learning by observation".
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