  Game theoretic methods have become popular for planning and prediction in
situations involving rich multi-agent interactions. However, these methods
often assume the existence of a single local Nash equilibria and are hence
unable to handle uncertainty in the intentions of different agents. While
maximum entropy (MaxEnt) dynamic games try to address this issue, practical
approaches solve for MaxEnt Nash equilibria using linear-quadratic
approximations which are restricted to unimodal responses and unsuitable for
scenarios with multiple local Nash equilibria. By reformulating the problem as
a POMDP, we propose MPOGames, a method for efficiently solving MaxEnt dynamic
games that captures the interactions between local Nash equilibria. We show the
importance of uncertainty-aware game theoretic methods via a two-agent merge
case study. Finally, we prove the real-time capabilities of our approach with
hardware experiments on a 1/10th scale car platform.
