An Empirical Study on Google Research Football Multi-agent Scenarios
文献类型：期刊论文
| 作者 | Yan Song6; He Jiang5 ; Zheng Tian4; Haifeng Zhang6; Yingping Zhang3; Jiangcheng Zhu3; Zonghong Dai3; Weinan Zhang2; Jun Wang1
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| 刊名 | Machine Intelligence Research
 |
| 出版日期 | 2024 |
| 卷号 | 21期号:3页码:549-570 |
| 关键词 | Multi-agent reinforcement learning (RL), distributed RL system, population-based training, reward shaping, game theory |
| ISSN号 | 2731-538X |
| DOI | 10.1007/s11633-023-1426-8 |
| 英文摘要 | Few 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 https://github.com/Shanghai-Digital-Brain-Laboratory/DB-Football. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/56482]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.University College London, London WC1E 6PT, UK 2.Shanghai Jiao Tong University, Shanghai 200001, China 3.Huawei Cloud, Guiyang 550003, China 4.ShanghaiTech University, Shanghai 200001, China 5.Digital Brain Lab, Shanghai 200001, China 6.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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