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arXiv · 2402.14174

Blending Data-Driven Priors in Dynamic Games

Justin Lidard·Haimin Hu·Asher Hancock·Zixu Zhang·Albert Gimó Contreras·Vikash Modi·Jonathan DeCastro·Deepak Gopinath·Guy Rosman·Naomi Ehrich Leonard·María Santos·Jaime Fernández Fisac
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Abstract

As intelligent robots like autonomous vehicles become increasingly deployed in the presence of people, the extent to which these systems should leverage model-based game-theoretic planners versus data-driven policies for safe, interaction-aware motion planning remains an open question. Existing dynamic game formulations assume all agents are task-driven and behave optimally. However, in reality, humans tend to deviate from the decisions prescribed by these models, and their behavior is better approximated under a noisy-rational paradigm. In this work, we investigate a principled methodology to blend a data-driven reference policy with an optimization-based game-theoretic policy. We formulate KLGame, an algorithm for solving non-cooperative dynamic game with Kullback-Leibler (KL) regularization with respect to a general, stochastic, and possibly multi-modal reference policy. Our method incorporates, for each decision maker, a tunable parameter that permits modulation between task-driven and data-driven behaviors. We propose an efficient algorithm for computing multi-modal approximate feedback Nash equilibrium strategies of KLGame in real time. Through a series of simulated and real-world autonomous driving scenarios, we demonstrate that KLGame policies can more effectively incorporate guidance from the reference policy and account for noisily-rational human behaviors versus non-regularized baselines. Website with additional information, videos, and code: https://kl-games.github.io/.

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BibTeXRIS

Justin Lidard, Haimin Hu, Asher Hancock, Zixu Zhang, Albert Gimó Contreras, Vikash Modi, Jonathan DeCastro, Deepak Gopinath, Guy Rosman, Naomi Ehrich Leonard, María Santos, Jaime Fernández Fisac. 2024-02-21. Blending Data-Driven Priors in Dynamic Games. https://arxiv.org/abs/2402.14174

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