On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions

• URL: http://arxiv.org/abs/2308.16539v2
• Date: Mon, 16 Oct 2023 14:37:54 GMT
• Title: On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions
• Authors: Christopher Diehl and Tobias Klosek and Martin Kr\"uger and Nils Murzyn and Torsten Bertram
• Abstract summary: We show a connection between differential games, optimal control, and energy-based models. Building upon this formulation, this work introduces a new end-to-end learning application. Experiments using simulated mobile robot pedestrian interactions and real-world automated driving data provide empirical evidence.
• Score: 0.13499500088995461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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