Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers

Authors

  • Philipp Geiger Bosch Center for Artificial Intelligence
  • Christoph-Nikolas Straehle Bosch Center for Artificial Intelligence

DOI:

https://doi.org/10.1609/aaai.v35i6.16628

Keywords:

Neuro-Symbolic AI (NSAI), Game Theory, Imitation Learning & Inverse Reinforcement Learning, Other Foundations of Multi Agent Systems

Abstract

For prediction of interacting agents' trajectories, we propose an end-to-end trainable architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable intermediate representations, and transfers to downstream decision making. It uses a net that reveals preferences from the agents' past joint trajectory, and a differentiable implicit layer that maps these preferences to local Nash equilibria, forming the modes of the predicted future trajectory. Additionally, it learns an equilibrium refinement concept. For tractability, we introduce a new class of continuous potential games and an equilibrium-separating partition of the action space. We provide theoretical results for explicit gradients and soundness. In experiments, we evaluate our approach on two real-world data sets, where we predict highway drivers' merging trajectories, and on a simple decision-making transfer task.

Downloads

Published

2021-05-18

How to Cite

Geiger, P., & Straehle, C.-N. (2021). Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers. Proceedings of the AAAI Conference on Artificial Intelligence, 35(6), 4950-4958. https://doi.org/10.1609/aaai.v35i6.16628

Issue

Section

AAAI Technical Track Focus Area on Neuro-Symbolic AI