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arXiv · 1801.07663

Inverse reinforcement learning in continuous time and space

Rushikesh Kamalapurkar
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Abstract

This paper develops a data-driven inverse reinforcement learning technique for a class of linear systems to estimate the cost function of an agent online, using input-output measurements. A simultaneous state and parameter estimator is utilized to facilitate output-feedback inverse reinforcement learning, and cost function estimation is achieved up to multiplication by a constant.

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Rushikesh Kamalapurkar. 2018-01-23. Inverse reinforcement learning in continuous time and space. https://doi.org/10.1109/ccta.2019.8920458

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