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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2304.13573 |
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| _version_ | 1866909084128641024 |
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| author | Bandyopadhyay, Soutrik Bhasin, Shubhendu |
| author_facet | Bandyopadhyay, Soutrik Bhasin, Shubhendu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_13573 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Safe Q-learning for continuous-time linear systems Bandyopadhyay, Soutrik Bhasin, Shubhendu Systems and Control 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. |
| title | Safe Q-learning for continuous-time linear systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2304.13573 |