Elo Ratings for Structural Credit Assignment in Multiagent Systems

In this paper we investigate the applications of Elo ratings (originally designed for 2-player chess) to a heterogeneous nonlinear multiagent system to determine an agent's overall impact on its team's performance. Measuring this impact has been attempted in many different ways, including reward shaping; the generation of heirarchies, holarchies, and teams; mechanism design; and the creation of subgoals. We show that in a multiagent system, an Elo rating will accurately reflect an agent's ability to contribute positively to a team's success with no need for any other feedback than a repeated binary win/loss signal. The Elo rating not only measures ``personal" success, but simultaneously success in assisting other agents to perform favorably.