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Environments with multi-agent interactions often result a rich set ofmodalities of behavior between agents due to the inherent suboptimality ofdecision making processes when agents settle for satisfactory decisions.However, existing algorithms for solving these dynamic games are strictlyunimodal and fail to capture the intricate multimodal behaviors of the agents.In this paper, we propose MMELQGames (Multimodal Maximum-Entropy LinearQuadratic Games), a novel constrained multimodal maximum entropy formulation ofthe Differential Dynamic Programming algorithm for solving generalized Nashequilibria. By formulating the problem as a certain dynamic game withincomplete and asymmetric information where agents are uncertain about the costand dynamics of the game itself, the proposed method is able to reason aboutmultiple local generalized Nash equilibria, enforce constraints with theAugmented Lagrangian framework and also perform Bayesian inference on thelatent mode from past observations. We assess the efficacy of the proposedalgorithm on two illustrative examples: multi-agent collision avoidance andautonomous racing. In particular, we show that only MMELQGames is able toeffectively block a rear vehicle when given a speed disadvantage and the rearvehicle can overtake from multiple positions.

Multimodal Maximum Entropy Dynamic Games