  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/.
