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

• URL: http://arxiv.org/abs/2310.13396v1
• Date: Fri, 20 Oct 2023 10:06:03 GMT
• Title: RL-X: A Deep Reinforcement Learning Library (not only) for RoboCup
• Authors: Nico Bohlinger and Klaus Dorer
• Abstract summary: RL-X provides a flexible and easy-to-extend with self-contained single directory algorithms. RL-X can reach up to 4.5x speedups compared to well-known frameworks like Stable-Baselines3.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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