Efficient Inverse Multiagent Learning

Denizalp Goktas, Amy Greenwald, Sadie Zhao, Alec Koppel, Sumitra Ganesh

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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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Primary Area: reinforcement learning

Submission Number: 8714