The contribution of this paper is a generalized formulation of correctional
learning using optimal transport, which is about how to optimally transport one
mass distribution to another. Correctional learning is a framework developed to
enhance the accuracy of parameter estimation processes by means of a
teacher-student approach. In this framework, an expert agent, referred to as
the teacher, modifies the data used by a learning agent, known as the student,
to improve its estimation process. The objective of the teacher is to alter the
data such that the student's estimation error is minimized, subject to a fixed
intervention budget. Compared to existing formulations of correctional
learning, our novel optimal transport approach provides several benefits. It
allows for the estimation of more complex characteristics as well as the
consideration of multiple intervention policies for the teacher. We evaluate
our approach on two theoretical examples, and on a human-robot interaction
application in which the teacher's role is to improve the robots performance in
an inverse reinforcement learning setting.

本文介绍了一种广义的纠正学习方法，在优化输运的框架下，利用一个专家代理来改善学习代理的参数估计，该方法可以估计更复杂的特征，并考虑了多个干预策略。

纠错学习的最优传输

Optimal Transport for Correctional Learning

To construct effective teaming strategies between humans and AI systems in
complex, risky situations requires an understanding of individual preferences
and behaviors of humans. Previously this problem has been treated in
case-specific or data-agnostic ways. In this paper, we build a framework
capable of capturing a compact latent representation of the human in terms of
their behavior and preferences based on data from a simulated population of
drivers. Our framework leverages, to the extent available, knowledge of
individual preferences and types from samples within the population to deploy
interaction policies appropriate for specific drivers. We then build a
lightweight simulation environment, HMIway-env, for modelling one form of
distracted driving behavior, and use it to generate data for different driver
types and train intervention policies. We finally use this environment to
quantify both the ability to discriminate drivers and the effectiveness of
intervention policies.

通过对司机人群行为偏好数据的建模和模拟，我们构建了一个智能辅助驾驶员驾驶干预策略的轻量级仿真环境，并使用该环境来测试不同驾驶员类型的区分能力和干预策略的效果。