@inproceedings{
nikulin2023xlandminigrid,
title={{XL}and-MiniGrid: Scalable Meta-Reinforcement Learning Environments in {JAX}},
author={Alexander Nikulin and Vladislav Kurenkov and Ilya Zisman and Viacheslav Sinii and Artem Agarkov and Sergey Kolesnikov},
booktitle={Intrinsically-Motivated and Open-Ended Learning Workshop @NeurIPS2023},
year={2023},
url={https://openreview.net/forum?id=xALDC4aHGz}
}
We present XLand-Minigrid, a suite of tools and grid-world environments for meta-reinforcement learning research inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid. XLand-Minigrid is written in JAX, designed to be highly scalable, and can potentially run on GPU or TPU accelerators, democratizing large-scale experimentation with limited resources. To demonstrate the generality of our library, we have implemented some well-known single-task environments as well as new meta-learning environments capable of generating $10^8$ distinct tasks. We have empirically shown that the proposed environments can scale up to $2^{13}$ parallel instances on the GPU, reaching tens of millions of steps per second.
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
Alexander Nikulin Vladislav Kurenkov Ilya Zisman Viacheslav Sinii Artem Agarkov Sergey Kolesnikov