Inverse reinforcement learning in continuous time and space
Systems and Control
2021-07-07 v1 Optimization and Control
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.
Cite
@article{arxiv.1801.07663,
title = {Inverse reinforcement learning in continuous time and space},
author = {Rushikesh Kamalapurkar},
journal= {arXiv preprint arXiv:1801.07663},
year = {2021}
}