  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 http://github.com/sotetsuk/pgx.
