Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers

• URL: http://arxiv.org/abs/2008.07303v7
• Date: Fri, 18 Feb 2022 15:32:04 GMT
• Title: Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers
• Authors: Philipp Geiger, Christoph-Nikolas Straehle
• Abstract summary: We propose an end-to-end trainable architecture that hybridizes neural nets with game-theoretic reasoning. For tractability, we introduce a new class of continuous potential games and an equilibrium-separating partition of the action space. We evaluate our approach on two real-world data sets, where we predict highway merging driver trajectories, and on a simple decision-making transfer task.
• Score: 9.594432031144716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 driver merging trajectories, and on a simple decision-making transfer task.

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