Generative Adversarial Inverse Multiagent Learning

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 a generative-adversarial (i.e., min-max) optimization problem, based on which we develop polynomial-time algorithms the solve them, 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 apprenticeship learning in polynomial time and number of samples, where we seek a simulacrum, i.e., parameters and an associated equilibrium, which replicate observations in expectation. We find that our approach outperforms other widely-used methods in predicting prices in Spanish electricity markets based on time-series data.