2 papers
cs.AI2026
Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX
Soichiro Nishimori, Shinri Okano, Keigo Habara +3
Riichi Mahjong is a multi-player, imperfect-information game characterized by stochasticity and high-dimensional state spaces. These attributes present a unique combination of chal…
cs.AI2023
Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning
Sotetsu Koyamada, Shinri Okano, Soichiro Nishimori +4
We propose Pgx, a suite of board game reinforcement learning (RL) environments written in JAX and optimized for GPU/TPU accelerators. By leveraging JAX's auto-vectorization and par…