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 parallelization over accelerators, Pgx can efficiently scale to thousands of simultaneous simulations over accelerators. In our experiments on a DGX-A100 workstation, we discovered that Pgx can simulate RL environments 10-100x faster than existing implementations available in Python. Pgx includes RL environments commonly used as benchmarks in RL research, such as backgammon, chess, shogi, and Go. Additionally, Pgx offers miniature game sets and baseline models to facilitate rapid research cycles. We demonstrate the efficient training of the Gumbel AlphaZero algorithm with Pgx environments. Overall, Pgx provides high-performance environment simulators for researchers to accelerate their RL experiments. Pgx is available at https://github.com/sotetsuk/pgx.

Numerous games have served as testbeds for artificial intelligence (AI) research to measure its progress. Mahjong is a highly challenging multi-agent imperfect information game with a vast player population. However, a challenge with using Mahjong as a testbed for AI is the lack of a publicly available framework that is fast, easy to use and implements popular rules for human players. We propose and describe Mjx, an open-source Mahjong framework, which implements one of the most popular Mahjong rules, riichi Mahjong (Japanese Mahjong). We compared the execution speed of Mjx with existing popular open-source software and demonstrated that it achieves 100x faster performance. Mjx is available at https://github.conmjx-project/mjx.