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Inverse Reinforcement Learning via Function Approximation for Clinical Motion Analysis

Li, Kun and Rath, Mrinal and Burdick, Joel W. (2018) Inverse Reinforcement Learning via Function Approximation for Clinical Motion Analysis. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE , Piscataway, NJ, pp. 610-617. ISBN 978-1-5386-3081-5. https://resolver.caltech.edu/CaltechAUTHORS:20180920-103333203

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Abstract

This paper introduces a new method for inverse reinforcement learning in large state spaces, where the learned reward function can be used to control high-dimensional robot systems and analyze complex human movement. To avoid solving the computationally expensive reinforcement learning problems in reward learning, we propose a function approximation method to ensure that the Bellman Optimality Equation always holds, and then estimate a function to maximize the likelihood of the observed motion. The time complexity of the proposed method is linearly proportional to the cardinality of the action set, thus it can handle large state spaces efficiently. We test the proposed method in a simulated environment on reward learning, and show that it is more accurate than existing methods and significantly better in scalability. We also show that the proposed method can extend many existing methods to large state spaces. We then apply the method to evaluating the effect of rehabilitative stimulations on patients with spinal cord injuries based on the observed patient motions.


Item Type:Book Section
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https://doi.org/10.1109/ICRA.2018.8460563DOIArticle
https://ieeexplore.ieee.org/document/8460563PublisherArticle
Additional Information:© 2018 IEEE. This work was supported by the National Institutes of Health, NIBIB.
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Record Number:CaltechAUTHORS:20180920-103333203
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180920-103333203
Official Citation:K. Li, M. Rath and J. W. Burdick, "Inverse Reinforcement Learning via Function Approximation for Clinical Motion Analysis," 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018, pp. 610-617. doi: 10.1109/ICRA.2018.8460563
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:89780
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:20 Sep 2018 17:39
Last Modified:03 Oct 2019 20:19

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