  Game theory offers an interpretable mathematical framework for modeling
multi-agent interactions. However, its applicability in real-world robotics
applications is hindered by several challenges, such as unknown agents'
preferences and goals. To address these challenges, we show a connection
between differential games, optimal control, and energy-based models and
demonstrate how existing approaches can be unified under our proposed
Energy-based Potential Game formulation. Building upon this formulation, this
work introduces a new end-to-end learning application that combines neural
networks for game-parameter inference with a differentiable game-theoretic
optimization layer, acting as an inductive bias. The experiments using
simulated mobile robot pedestrian interactions and real-world automated driving
data provide empirical evidence that the game-theoretic layer improves the
predictive performance of various neural network backbones.
