5 papers · 1 filter
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…
A Simple, Solid, and Reproducible Baseline for Bridge Bidding AI
Haruka Kita, Sotetsu Koyamada, Yotaro Yamaguchi +1
Contract bridge, a cooperative game characterized by imperfect information and multi-agent dynamics, poses significant challenges and serves as a critical benchmark in artificial i…
End-to-End Policy Gradient Method for POMDPs and Explainable Agents
Soichiro Nishimori, Sotetsu Koyamada, Shin Ishii
Real-world decision-making problems are often partially observable, and many can be formulated as a Partially Observable Markov Decision Process (POMDP). When we apply reinforcemen…
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…
Suphx: Mastering Mahjong with Deep Reinforcement Learning
Junjie Li, Sotetsu Koyamada, Qiwei Ye +7
Artificial Intelligence (AI) has achieved great success in many domains, and game AI is widely regarded as its beachhead since the dawn of AI. In recent years, studies on game AI h…