2 research outputs found
Pgx: Hardware-accelerated Parallel Game Simulators for Reinforcement Learning
We propose Pgx, a suite of board game reinforcement learning (RL)
environments written in JAX and optimized for GPU/TPU accelerators. By
leveraging auto-vectorization and Just-In-Time (JIT) compilation of JAX, Pgx
can efficiently scale to thousands of parallel executions over accelerators. In
our experiments on a DGX-A100 workstation, we discovered that Pgx can simulate
RL environments 10-100x faster than existing Python RL libraries. 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.Comment: 9 page