2021/12/07 by Meng Yao, Yao Meng, Qiyue Yin +14
Computer Science · Social Sciences · #Artificial Intelligence in Games #Evolutionary Game Theory and Cooperation #FOS: Computer and information sciences #Multiagent Systems (cs.MA) #Reinforcement Learning in Robotics #cs.MA
paper · pdf · doi:10.48550/arxiv.2112.03809
arxiv created 2021/12/07 · openalex publication_date 2021/12/07 · arxiv updated 2021/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Multi-agent reinforcement learning (MARL) has received increasing attention for its applications in various domains. Researchers have paid much attention on its partially observable and cooperative settings for meeting real-world requirements. For testing performance of different algorithms, standardized environments are designed such as the StarCraft Multi-Agent Challenge, which is one of the most successful MARL benchmarks. To our best knowledge, most of current environments are synchronous, where agents execute actions in the same pace. However, heterogeneous agents usually have their own action spaces and there is no guarantee for actions from different agents to have the same executed cycle, which leads to asynchronous multi-agent cooperation. Inspired from the Wargame, a confrontation game between two armies abstracted from real world environment, we propose the first Partially Observable Asynchronous multi-agent Cooperation challenge (POAC) for the MARL community. Specifically, POAC supports two teams of heterogeneous agents to fight with each other, where an agent selects actions based on its own observations and cooperates asynchronously with its allies. Moreover, POAC is a light weight, flexible and easy to use environment, which can be configured by users to meet different experimental requirements such as self-play model, human-AI model and so on. Along with our benchmark, we offer six game scenarios of varying difficulties with the built-in rule-based AI as opponents. Finally, since most MARL algorithms are designed for synchronous agents, we revise several representatives to meet the asynchronous setting, and the relatively poor experimental results validate the challenge of POAC. Source code is released in \urlhttp://turingai.ia.ac.cn/data_center/show.