  This work uses game theory as a mathematical framework to address interaction
modeling in multi-agent motion forecasting and control. Despite its
interpretability, applying game theory to real-world robotics, like automated
driving, faces challenges such as unknown game parameters. To tackle these, we
establish a connection between differential games, optimal control, and
energy-based models, demonstrating how existing approaches can be unified under
our proposed Energy-based Potential Game formulation. Building upon this, we
introduce 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 analysis provides empirical evidence
that the game-theoretic layer adds interpretability and improves the predictive
performance of various neural network backbones using two simulations and two
real-world driving datasets.
