RL-X: A Deep Reinforcement Learning Library (not only) for RoboCup
Voices Powered by
Connected to paperThis paper is a preprint and has not been certified by peer review
RL-X: A Deep Reinforcement Learning Library (not only) for RoboCup
Nico Bohlinger, Klaus Dorer
AbstractThis 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.