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• W3113168424 endingPage "4958" @default.
• W3113168424 startingPage "4950" @default.
• W3113168424 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." @default.
• W3113168424 created "2020-12-21" @default.
• W3113168424 creator A5017092404 @default.
• W3113168424 creator A5074004981 @default.
• W3113168424 date "2021-05-18" @default.
• W3113168424 modified "2023-09-26" @default.
• W3113168424 title "Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers" @default.
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• W3113168424 doi "https://doi.org/10.1609/aaai.v35i6.16628" @default.
• W3113168424 hasPublicationYear "2021" @default.
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