RL-X: A Deep Reinforcement Learning Library (not only) for RoboCup

2023-10-20Code Available2· sign in to hype

Nico Bohlinger, Klaus Dorer

Code Available — Be the first to reproduce this paper.

Code

github.com/nico-bohlinger/rl-x
OfficialIn paperjax★ 252

Abstract

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.

Tasks

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning

RL-X: A Deep Reinforcement Learning Library (not only) for RoboCup

Code

Abstract

Tasks

Reproductions