Energy-based Potential Games for Joint Motion Forecasting and Control

• URL: http://arxiv.org/abs/2312.01811v1
• Date: Mon, 4 Dec 2023 11:30:26 GMT
• Title: Energy-based Potential Games for Joint Motion Forecasting and Control
• Authors: Christopher Diehl, Tobias Klosek, Martin Kr\"uger, Nils Murzyn, Timo Osterburg, Torsten Bertram
• Abstract summary: This work uses game theory as a mathematical framework to address interaction modeling in motion forecasting and control. 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. 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.
• Score: 0.125828876338076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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