Efficient Inverse Multiagent Learning

Published: 16 Jan 2024, Last Modified: 16 Apr 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: Inverse Game Theory, Inverse Multiagent Reinforcement Learning
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TL;DR: We provide an efficient generative adversarial characterization for the inference of parameters of a game in inverse game theory and inverse multiagent (reinforcement) learning settings.
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.
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Supplementary Material: pdf
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Primary Area: reinforcement learning
Submission Number: 8714
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