RL-X: A Deep Reinforcement Learning Library (Not Only) for RoboCup

This paper presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5 \(\times \) speedups compared to well-known frameworks like Stable-Baselines3.