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An Empirical Study on Google Research Football Multi-agent Scenarios
Yan Song1; He Jiang2; Zheng Tian3; Haifeng Zhang1; Yingping Zhang4; Jiangcheng Zhu4; Zonghong Dai4; Weinan Zhang5; Jun Wang6
Source PublicationMachine Intelligence Research
AbstractFew multi-agent reinforcement learning (MARL) researches on Google research football (GRF)[1] focus on the 11-vs-11 multi-agent full-game scenario and to the best of our knowledge, no open benchmark on this scenario has been released to the public. In this work, we fill the gap by providing a population-based MARL training pipeline and hyperparameter settings on multi-agent football scenario that outperforms the bot with difficulty 1.0 from scratch within 2 million steps. Our experiments serve as a reference for the expected performance of independent proximal policy optimization (IPPO)[2], a state-of-the-art multi-agent reinforcement learning algorithm where each agent tries to maximize its own policy independently across various training configurations. Meanwhile, we release our training framework Light-MALib which extends the MALib[3] codebase by distributed and asynchronous implementation with additional analytical tools for football games. Finally, we provide guidance for building strong football AI with population-based training[4] and release diverse pretrained policies for benchmarking. The goal is to provide the community with a head start for whoever experiment their works on GRF and a simple-to-use population-based training framework for further improving their agents through self-play. The implementation is available at
KeywordMulti-agent reinforcement learning (RL), distributed RL system, population-based training, reward shaping, game theory
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Document Type期刊论文
Collection学术期刊_Machine Intelligence Research
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.Digital Brain Lab, Shanghai 200001, China
3.ShanghaiTech University, Shanghai 200001, China
4.Huawei Cloud, Guiyang 550003, China
5.Shanghai Jiao Tong University, Shanghai 200001, China
6.University College London, London WC1E 6PT, UK
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yan Song,He Jiang,Zheng Tian,et al. An Empirical Study on Google Research Football Multi-agent Scenarios[J]. Machine Intelligence Research,2024,21(3):549-570.
APA Yan Song.,He Jiang.,Zheng Tian.,Haifeng Zhang.,Yingping Zhang.,...&Jun Wang.(2024).An Empirical Study on Google Research Football Multi-agent Scenarios.Machine Intelligence Research,21(3),549-570.
MLA Yan Song,et al."An Empirical Study on Google Research Football Multi-agent Scenarios".Machine Intelligence Research 21.3(2024):549-570.
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