A Function Approximation Method for Model-based High-Dimensional Inverse Reinforcement Learning

This works handles the inverse reinforcement learning problem in high-dimensional state spaces, which relies on an efficient solution of model-based high-dimensional reinforcement learning problems. To solve the computationally expensive reinforcement learning problems, we propose a function approxi...

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Bibliographic Details
Published inarXiv.org
Main Authors Li, Kun, Burdick, Joel W
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 23.08.2017
Subjects
Approximation
Computer simulation
Handles
Machine learning
Markov analysis
Mathematical analysis
Test procedures
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Summary:This works handles the inverse reinforcement learning problem in high-dimensional state spaces, which relies on an efficient solution of model-based high-dimensional reinforcement learning problems. To solve the computationally expensive reinforcement learning problems, we propose a function approximation method to ensure that the Bellman Optimality Equation always holds, and then estimate a function based on the observed human actions for inverse reinforcement learning problems. The time complexity of the proposed method is linearly proportional to the cardinality of the action set, thus it can handle high-dimensional even continuous state spaces efficiently. We test the proposed method in a simulated environment to show its accuracy, and three clinical tasks to show how it can be used to evaluate a doctor's proficiency.
ISSN:2331-8422