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.

翻译：博弈论为研究多个决策者之间的互动提供了一种数学方法。然而，由于策略数量庞大，经典博弈论分析在可扩展性方面存在局限，难以直接应用于更复杂的场景。本综述全面概述了一种用于大规模博弈的框架，即策略空间响应预言机（PSRO），该框架通过将注意力集中在策略的充分子集上，有望提升可扩展性。我们首先阐述PSRO的动机并提供历史背景。随后，我们聚焦于PSRO的策略探索问题：即以最小计算成本构建仍能良好表征原始博弈的有效策略子集所面临的挑战。我们综述了当前提升PSRO效率的研究方向，并探讨了PSRO在不同领域的应用。最后，我们讨论了开放性问题与未来研究方向。