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Feb, 2025
高效的逆多智能体学习
Efficient Inverse Multiagent Learning
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Denizalp Goktas, Amy Greenwald, Sadie Zhao, Alec Koppel, Sumitra Ganesh
TL;DR
本研究应对逆博弈理论和逆多智能体学习中寻找博弈收益函数参数的挑战，使得期望行为达到均衡。我们提出了将这些问题表述为生成对抗（即最小-最大）优化问题的方法，并开发了多项式时间算法。研究结果表明，该方法在基于时间序列数据的西班牙电力市场价格预测中优于广泛使用的ARIMA方法。
Abstract
In this paper, we study
Inverse Game Theory
(resp. inverse
Multiagent Learning
) in which the goal is to find parameters of a game's payoff functions for which the expected (resp. sampled) behavior is an equilibri
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