A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning
This work addresses a specific problem in cooperative learning systems for researchers in machine learning and control theory, but it appears incremental as it builds on existing correctional learning concepts.
The paper tackles the problem of improving estimation accuracy in a teacher-student correctional learning framework by reducing variance in the student's estimate, and it formulates an online decision-making problem as a Markov decision process to derive an optimal policy, validated through numerical experiments.
A classical learning setting typically concerns an agent/student who collects data, or observations, from a system in order to estimate a certain property of interest. Correctional learning is a type of cooperative teacher-student framework where a teacher, who has partial knowledge about the system, has the ability to observe and alter (correct) the observations received by the student in order to improve the accuracy of its estimate. In this paper, we show how the variance of the estimate of the student can be reduced with the help of the teacher. We formulate the corresponding online problem - where the teacher has to decide, at each time instant, whether or not to change the observations due to a limited budget - as a Markov decision process, from which the optimal policy is derived using dynamic programming. We validate the framework in numerical experiments, and compare the optimal online policy with the one from the batch setting.