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Sökning: WFRF:(Winqvist R) > Konferensbidrag

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  • Lourenço, Inês, 1994-, et al. (författare)
  • A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning
  • 2022
  • Ingår i: 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3456-3461
  • Konferensbidrag (refereegranskat)abstract
    • 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.
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  • Winqvist, Rebecka, et al. (författare)
  • Optimal Transport for Correctional Learning
  • 2023
  • Ingår i: 2023 62nd IEEE Conference on Decision and Control, CDC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 6806-6812
  • Konferensbidrag (refereegranskat)abstract
    • 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.
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  • Resultat 1-9 av 9

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