RL-X: A Deep Reinforcement Learning Library (not only) for RoboCup
Nico Bohlinger Klaus Dorer
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 4.5x speedups compared to well-known
frameworks like Stable-Baselines3.