This is the pre-proceedings for the RLC 2026. You may expect minor changes.
Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
Will be presented at the Reinforcement Learning Conference (RLC), Montréal, Quebec, Canada, August 15–17, 2026.
Keywords: Games, hardware acceleration, description languages
Games have long been used as benchmarks and testing environments for research in artificial intelligence. A key step in supporting this research was the development of game description languages: frameworks that compile domain-specific code into playable and simulatable game environments, allowing researchers to generalize their algorithms and approaches across multiple games without having to manually implement each one. More recently, progress in reinforcement learning (RL) has been greatly aided by advances in hardware acceleration. Libraries like JAX allow practitioners to take full advantage of cutting-edge computing hardware, often speeding up training and testing by orders of magnitude. Here, we present a synthesis of these strands of research: a domain-specific language for board games which automatically compiles into hardware-accelerated code. Our framework, Ludax, combines the generality of game description languages with the speed of modern parallel processing hardware and is designed to fit neatly into existing deep learning pipelines. We envision Ludax as a tool to help accelerate games research generally, from RL to cognitive science, by enabling rapid simulation and providing a flexible representation scheme. We present technical notes on the description language and compilation process, along with speed benchmarking.
Graham Todd, Alexander George Padula, Dennis J. N. J. Soemers, Sam Earle, and Julian Togelius. "Ludax: A GPU-Accelerated Description Language for Board Games." Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
BibTeX:@article{todd2026ludax,
title={Ludax: A GPU-Accelerated Description Language for Board Games},
author={Graham Todd and Alexander George Padula and Dennis J. N. J. Soemers and Sam Earle and Julian Togelius},
journal={Reinforcement Learning Journal},
volume={7},
pages={},
year={2026}
}