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JaxUED: A simple and useable UED library in Jax

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arxiv 2403.13091 v1 pith:RE2TVNLI submitted 2024-03-19 cs.LG cs.AI

Samuel Coward , Michael Beukman , Jakob Foerster This is my paper · ORCID
classification cs.LGcs.AI
keywords jaxuedimplementationslibraryacceleratingaccelerationalgorithmsbaselinecleanrl
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We present JaxUED, an open-source library providing minimal dependency implementations of modern Unsupervised Environment Design (UED) algorithms in Jax. JaxUED leverages hardware acceleration to obtain on the order of 100x speedups compared to prior, CPU-based implementations. Inspired by CleanRL, we provide fast, clear, understandable, and easily modifiable implementations, with the aim of accelerating research into UED. This paper describes our library and contains baseline results. Code can be found at https://github.com/DramaCow/jaxued.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning

    cs.MA 2026-02 unverdicted novelty 6.0 of 10

    TABX is a JAX-based, GPU-accelerated, configurable multi-agent battle simulator that lets researchers vary units, terrain, and physics to benchmark cooperative MARL algorithms.

  2. An Optimisation Framework for Unsupervised Environment Design

    cs.LG 2025-05 conditional novelty 6.0 of 10

    UED is reframed as an entropy-regularized minimax problem with two-timescale gradient convergence guarantees for zero-sum scores, and a generalized learnability score improves robustness on three benchmarks, with the ...

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