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, a type of 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
multimodal 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.

智能机器人在与人类一起使用的情况下，研究了基于数据驱动和优化的博弈论策略结合的动态规划方法，以更有效地进行交互感知运动规划。

动态游戏中数据驱动的先验融合

Blending Data-Driven Priors in Dynamic Games

Glycemic control is essential for critical care. However, it is a challenging
task because there has been no study on personalized optimal strategies for
glycemic control. This work aims to learn personalized optimal glycemic
trajectories for severely ill septic patients by learning data-driven policies
to identify optimal targeted blood glucose levels as a reference for
clinicians. We encoded patient states using a sparse autoencoder and adopted a
reinforcement learning paradigm using policy iteration to learn the optimal
policy from data. We also estimated the expected return following the policy
learned from the recorded glycemic trajectories, which yielded a function
indicating the relationship between real blood glucose values and 90-day
mortality rates. This suggests that the learned optimal policy could reduce the
patients' estimated 90-day mortality rate by 6.3%, from 31% to 24.7%. The
result demonstrates that reinforcement learning with appropriate patient state
encoding can potentially provide optimal glycemic trajectories and allow
clinicians to design a personalized strategy for glycemic control in septic
patients.

本研究使用自编码器和强化学习方法，通过学习数据驱动的策略，确定重症感染病人的个性化最佳血糖控制方法，建立血糖水平与预计 90 天死亡率的关系，从而减少患者的预测死亡率。