Computational Rationalization: The Inverse Equilibrium Problem

TL;DR

This paper introduces a novel inverse equilibrium approach for modeling and predicting the behavior of agents in multi-agent settings, extending inverse optimal control to competitive and cooperative environments.

Contribution

It develops a new method combining game-theoretic regret and maximum entropy principles to recover reward functions and predict multi-agent behavior.

Findings

Successfully predicts multi-agent behavior in various scenarios.

Recovers underlying reward functions from observed actions.

Extends inverse optimal control to multi-agent domains.

Abstract

Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the single-agent decision-theoretic setting, inverse optimal control techniques assume that observed behavior is an approximately optimal solution to an unknown decision problem. These techniques learn a utility function that explains the example behavior and can then be used to accurately predict or imitate future behavior in similar observed or unobserved situations. In this work, we consider similar tasks in competitive and cooperative multi-agent domains. Here, unlike single-agent settings, a player cannot myopically maximize its reward --- it must speculate on how the other agents may act to influence the game's outcome. Employing the game-theoretic notion of regret and the principle of maximum entropy, we introduce a technique for predicting and generalizing behavior, as well as recovering a reward function in these domains.