Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX
Mahjax is a fully vectorized Riichi Mahjong environment implemented in JAX, enabling large-scale rollout parallelization on GPUs. It achieves throughputs of up to 2 million and 1 million steps per second on eight NVIDIA A100 GPUs under no-red and red rules, respectively. Designed for tabula rasa reinforcement learning, it also includes a visualization tool. Experiments show agents can effectively improve their rank against baseline policies.
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Key points
- Mahjax is a fully vectorized Riichi Mahjong simulator based on JAX for GPU parallelization.
- It achieves up to 2 million steps per second on 8 NVIDIA A100 GPUs (no-red rule).
- Enables tabula rasa reinforcement learning without human data.
- Includes a visualization tool for debugging and agent interaction.
Why it matters
This matters because mahjax is a fully vectorized Riichi Mahjong simulator based on JAX for GPU parallelization.
Technical impact
May affect agent architecture, tool calling, workflow automation, and product integration.
[2605.20577] Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX
[Submitted on 20 May 2026]
Title:Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX
View a PDF of the paper titled Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX, by Soichiro Nishimori and 5 other authors
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Abstract: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.
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.20577 [cs.AI]
(or arXiv:2605.20577v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.20577
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Soichiro Nishimori [view email] [v1] Wed, 20 May 2026 00:33:28 UTC (217 KB)
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