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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