Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some studies have made progress in ZSC by exposing the agents to diverse partners during the training process. They usually involve self-play when training the partners, implicitly assuming that the tasks are homogeneous. However, many real-world tasks are heterogeneous, and hence previous methods may be inefficient. In this paper, we study the heterogeneous ZSC problem for the first time and propose a general method based on coevolution, which coevolves two populations of agents and partners through three sub-processes: pairing, updating and selection. Experimental results on various heterogeneous tasks highlight the necessity of considering the heterogeneous setting and demonstrate that our proposed method is a promising solution for heterogeneous ZSC tasks.
@article{arxiv.2208.04957,
title = {Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution},
author = {Ke Xue and Yutong Wang and Cong Guan and Lei Yuan and Haobo Fu and Qiang Fu and Chao Qian and Yang Yu},
journal= {arXiv preprint arXiv:2208.04957},
year = {2025}
}