Efficient Inverse Multiagent Learning
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 equilibrium. We formulate these problems as generative-adversarial (i.e., min-max) optimization problems, which we develop polynomial-time algorithms to solve, the former of which relies on an exact first- order oracle, and the latter, a stochastic one. We extend our approach to solve inverse multiagent simulacral learning in polynomial time and number of samples. In these problems, we seek a simulacrum, meaning parameters and an associated equilibrium that replicate the given observations in expectation. We find that our approach outperforms the widely-used ARIMA method in predicting prices in Spanish electricity markets based on time-series data.
Cite
Text
Goktas et al. "Efficient Inverse Multiagent Learning." International Conference on Learning Representations, 2024.Markdown
[Goktas et al. "Efficient Inverse Multiagent Learning." International Conference on Learning Representations, 2024.](https://mlanthology.org/iclr/2024/goktas2024iclr-efficient/)BibTeX
@inproceedings{goktas2024iclr-efficient,
title = {{Efficient Inverse Multiagent Learning}},
author = {Goktas, Denizalp and Greenwald, Amy and Zhao, Sadie and Koppel, Alec and Ganesh, Sumitra},
booktitle = {International Conference on Learning Representations},
year = {2024},
url = {https://mlanthology.org/iclr/2024/goktas2024iclr-efficient/}
}