Policy Space Response Oracles: A Survey
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866909210833321984 |
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| author | Bighashdel, Ariyan Wang, Yongzhao McAleer, Stephen Savani, Rahul Oliehoek, Frans A. |
| author_facet | Bighashdel, Ariyan Wang, Yongzhao McAleer, Stephen Savani, Rahul Oliehoek, Frans A. |
| contents | Game theory provides a mathematical way to study the interaction between multiple decision makers. However, classical game-theoretic analysis is limited in scalability due to the large number of strategies, precluding direct application to more complex scenarios. This survey provides a comprehensive overview of a framework for large games, known as Policy Space Response Oracles (PSRO), which holds promise to improve scalability by focusing attention on sufficient subsets of strategies. We first motivate PSRO and provide historical context. We then focus on the strategy exploration problem for PSRO: the challenge of assembling effective subsets of strategies that still represent the original game well with minimum computational cost. We survey current research directions for enhancing the efficiency of PSRO, and explore the applications of PSRO across various domains. We conclude by discussing open questions and future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02227 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Policy Space Response Oracles: A Survey Bighashdel, Ariyan Wang, Yongzhao McAleer, Stephen Savani, Rahul Oliehoek, Frans A. Computer Science and Game Theory Artificial Intelligence Multiagent Systems Game theory provides a mathematical way to study the interaction between multiple decision makers. However, classical game-theoretic analysis is limited in scalability due to the large number of strategies, precluding direct application to more complex scenarios. This survey provides a comprehensive overview of a framework for large games, known as Policy Space Response Oracles (PSRO), which holds promise to improve scalability by focusing attention on sufficient subsets of strategies. We first motivate PSRO and provide historical context. We then focus on the strategy exploration problem for PSRO: the challenge of assembling effective subsets of strategies that still represent the original game well with minimum computational cost. We survey current research directions for enhancing the efficiency of PSRO, and explore the applications of PSRO across various domains. We conclude by discussing open questions and future research. |
| title | Policy Space Response Oracles: A Survey |
| topic | Computer Science and Game Theory Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2403.02227 |