Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX

Riichi Mahjong is a multi-player, imperfect-information game characterized by stochasticity and high-dimensional state spaces. These attributes present a unique combination of challenges that mirror complex real-world decision-making problems in reinforcement learning. While prior research has heavily relied on supervised learning from human play logs to pre-train the policy, algorithms capable of learning \textit{tabula rasa} (from scratch) offer greater potential for general applicability, as evidenced by the AlphaZero lineage. To facilitate such research, we introduce \textbf{Mahjax}, a fully vectorized Riichi Mahjong environment implemented in JAX to enable large-scale rollout parallelization on Graphics Processing Units (GPUs). We also provide a high-quality visualization tool to streamline debugging and interaction with trained agents. Experimental results demonstrate that Mahjax achieves throughputs of up to \textbf{2 million} and \textbf{1 million steps per second} on eight NVIDIA A100 GPUs under the no-red and red rules, respectively. Furthermore, we validate the environment's utility for reinforcement learning by showing that agents can be trained effectively to improve their rank against baseline policies.

Pgx: Hardware-accelerated parallel game simulation for reinforcement learning

We propose Pgx, a collection of board game simulators written in JAX. Thanks to auto-vectorization and Just-In-Time compilation of JAX, Pgx scales easily to thousands of parallel execution on GPU/TPU accelerators. We found that the simulation of Pgx on a single A100 GPU is 10x faster than that of existing reinforcement learning libraries. Pgx implements games considered vital benchmarks in artificial intelligence research, such as Backgammon, Shogi, and Go. Pgx is available at https://github.com/sotetsuk/pgx.