ArenaSim: A High-Performance Simulation Platform for Multi-Robot Self-Play Learning

Yuxin Ke, Shaohui Li, Zhi Li, Haoran Li, Yu Liu, You He

Abstract: In this letter, we introduce ArenaSim, a novel simulation platform designed for realistic and efficient self-play learning in multi-robot cooperative-competitive games. Compared to previous simulation platforms designed for the same task, we achieve fine-grained simulation of the robots with rotatable gimbals and roller-independent mecanum wheels in ArenaSim and validate its fidelity through real-world experiments. To inspire further exploration of this simulation platform, we design a hierarchical structure to address the cooperative-competitive game. The hierarchical structure is composed of a high-level strategy that generates macro actions such as moving and shooting, and a low-level controller that translates these macro actions into precise motion control. Furthermore, we evaluate several self-play algorithms in ArenaSim and present a benchmark. The experiments show that the multi-robot cooperative-competitive game is still challenging for self-play learning. We hope that ArenaSim can further inspire research on self-play learning and multi-robot cooperative-competitive games.

External IDs:dblp:journals/ral/KeLLLLH25