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Collaborative Research: Machine Learning Theory and Algorithms for Differential Games, with Applications in Economics

合作研究：微分博弈的机器学习理论和算法及其在经济学中的应用

基本信息

批准号：
1953035
负责人：
Ruimeng Hu
金额：
$ 20万
依托单位：
University of California-Santa Barbara
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953035&HistoricalAwards=false
关键词：
Collaborative Research Machine Learning Theory

项目摘要

Artificial intelligence (AI) has been applied in many scientific fields, including imaging, computer vision, and materials science. However, the study of the application of AI to differential games and economics is still in its infancy. Differential games, as an offspring of game theory and optimal control, provide the modeling and analysis of conflicts in the context of a dynamical systems. Domains of applications include management science, economics, social science, biology, and national security. One of the core objectives is to compute Nash equilibria that refer to strategies by which no player has an incentive to deviate. The research aims to break the tractability barrier in computing these Nash equilibria by using, developing, and studying appropriate Machine Learning algorithms. The project also provides research training opportunities for graduate students. A major bottleneck comes from the notorious intractability of finite-player games, which makes the direct computation of Nash equilibria extremely time-consuming and memory demanding, especially for a large number of players. The problem of efficiently and accurately computing Nash equilibria for stochastic differential games with a finite number of heterogeneous players is addressed by developing play-based Deep Neural Networks algorithms. Infinite-player games will be solved by new Reinforcement Learning algorithms developed in the context of Mean Field Game theory for competitive games and Mean Field Control theory for cooperative games. Applications to economics and finance problems such as Systemic Risk and Investment/Consumption are considered.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人工智能（AI）已应用于许多科学领域，包括成像，计算机视觉和材料科学。然而，人工智能在微分博弈和经济学中的应用研究仍处于起步阶段。微分对策作为对策论和最优控制的产物，提供了动态系统背景下冲突的建模和分析。应用领域包括管理科学、经济学、社会科学、生物学和国家安全。核心目标之一是计算纳什均衡，即没有参与者有动机偏离的策略。该研究旨在通过使用，开发和研究适当的机器学习算法来打破计算这些纳什均衡的易处理性障碍。该项目还为研究生提供研究培训机会。一个主要的瓶颈来自于众所周知的棘手的有限玩家游戏，这使得纳什均衡的直接计算非常耗时和内存需求，特别是对于大量的玩家。通过开发基于游戏的深度神经网络算法，解决了具有有限数量异构玩家的随机微分游戏的纳什均衡的有效和准确计算问题。无限玩家游戏将通过新的强化学习算法来解决，该算法是在竞争游戏的平均场游戏理论和合作游戏的平均场控制理论的背景下开发的。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。