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arXiv · 2304.13573

Safe Q-learning for continuous-time linear systems

Soutrik Bandyopadhyay·Shubhendu Bhasin
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Abstract

Q-learning is a promising method for solving optimal control problems for uncertain systems without the explicit need for system identification. However, approaches for continuous-time Q-learning have limited provable safety guarantees, which restrict their applicability to real-time safety-critical systems. This paper proposes a safe Q-learning algorithm for partially unknown linear time-invariant systems to solve the linear quadratic regulator problem with user-defined state constraints. We frame the safe Q-learning problem as a constrained optimal control problem using reciprocal control barrier functions and show that such an extension provides a safety-assured control policy. To the best of our knowledge, Q-learning for continuous-time systems with state constraints has not yet been reported in the literature.

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Soutrik Bandyopadhyay, Shubhendu Bhasin. 2023-04-26. Safe Q-learning for continuous-time linear systems. https://arxiv.org/abs/2304.13573

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