{"type":"ScholarlyArticle","title":"Self-adaptive PSRO: Towards an Automatic Population-based Game Solver","authors":[{"name":"Pengdeng Li","slug":"pengdeng-li","given":"Pengdeng","family":"Li","affiliation":"Nanyang Technological University , Singapore"},{"name":"Shuxin Li","slug":"shuxin-li","given":"Shuxin","family":"Li","affiliation":"Nanyang Technological University , Singapore"},{"name":"Chang Yang","slug":"chang-yang","given":"Chang","family":"Yang","affiliation":"The Hong Kong Polytechnic University , Hong Kong SAR , China"},{"name":"Xinrun Wang","slug":"xinrun-wang","given":"Xinrun","family":"Wang","affiliation":"Nanyang Technological University , Singapore"},{"name":"Xiao Huang","slug":"xiao-huang","given":"Xiao","family":"Huang","affiliation":"The Hong Kong Polytechnic University , Hong Kong SAR , China"},{"name":"Hau Chan","slug":"hau-chan","given":"Hau","family":"Chan","affiliation":"University of Nebraska-Lincoln , Lincoln , Nebraska , United States"},{"name":"Bo An","slug":"bo-an","given":"Bo","family":"An","affiliation":"Nanyang Technological University , Singapore; Skywork AI , Singapore"}],"abstract":"Policy-Space Response Oracles (PSRO) as a general algorithmic framework has achieved state-of-the-art performance in learning equilibrium policies of two-player zero-sum games. However, the hand-crafted hyperparameter value selection in most of the existing works requires extensive domain knowledge, forming the main barrier to applying PSRO to different games. In this work, we make the first attempt to investigate the possibility of self-adaptively determining the optimal hyperparameter values in the PSRO framework. Our contributions are three-fold: (1) Using several hyperparameters, we propose a parametric PSRO that unifies the gradient descent ascent (GDA) and different PSRO variants. (2) We propose the self-adaptive PSRO (SPSRO) by casting the hyperparameter value selection of the parametric PSRO as a hyperparameter optimization (HPO) problem where our objective is to learn an HPO policy that can self-adaptively determine the optimal hyperparameter values during the running of the parametric PSRO. (3) To overcome the poor performance of online HPO methods, we propose a novel offline HPO approach to optimize the HPO policy based on the Transformer architecture. Experiments on various two-player zero-sum games demonstrate the superiority of SPSRO over different baselines.","doi":"10.24963/ijcai.2024/16","url":"https://ijcai.org/proceedings/2024/16","pdf":"https://ijcai.org/proceedings/2024/0016.pdf","markdown":"https://ijcai.org/proceedings/2024/16.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/16","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":16,"pages":{"first":139,"last":147},"keywords":["Agent-based and Multi-agent Systems: MAS: Multi-agent learning","Machine Learning: ML: Hyperparameter optimization","Machine Learning: ML: Game Theory","Game Theory and Economic Paradigms: GTEP: Noncooperative games"],"videos":[],"references":[{"position":1,"raw":"[Akiba et al., 2019] Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 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