Eintrag in der Universitätsbibliographie der TU Chemnitz
Volltext zugänglich unter
URN: urn:nbn:de:bsz:ch1-qucosa2-968096
Osinenko, Pavel
Streif, Stefan ; Faulwasser, Timm ; Görges, Daniel (Gutachter)
Reinforcement Learning with Guarantees
Verstärkendes Lernen mit Garantien
Kurzfassung in englisch
This dissertation summarizes my scientific work originating from 2015, focusing on reinforcement learning and subjects that surrounded it. Three major research tracks are featured in this dissertation:- Lyapunov-based reinforcement learning with guarantees with its apex approach called CALF ('Critic as Lyapunov Function').
- Predictive reinforcement learning with guarantees, so-called 'stacked reinforcement earning'.
- Novel approaches to formal control system analysis, whose tools were widely employed the above two in their evolution.
Whereas each track comprises multiple research papers produces throughout the years, for brevity and ease of technical exposition, only CALF is detailed in this dissertation, with only a brief account of stacked reinforcement learning. The reader should consult the referenced works, whose list is given in Section 7.1.
The outline of this dissertation is as follows:
Section 4 and Section 5 give a brief sketch of history of modern control and, respectively, reinforcement learning.
Section 7 summarizes the major contributions and lists the key publications related to the dissertation.
Section 8 overviews the contemporary approaches to reinforcement learning guarantees and briefly analyses them.
Section 9 presents a detailed account of CALF.
Section 10 is dedicated to stacked reinforcement learning.
Section 11 presents the studies in stabilization, constructive control and overviews some selected application works.
The main text is concluded by a summary and research outlook into future possible directions. The CALF analysis if presented in the appendix.
We begin with a short overview of modern automatic control history, with a focus on adaptive and optimal control, and then proceed to a sketch of history of artificial intelligence, emphasizing reinforcement learning and its interplay with automatic control. We believe that precisely the optimal and adaptive control fields feature the most notable overlap with reinforcement learning generally, and reinforcement learning with guarantees in particular.
| Universität: | Technische Universität Chemnitz | |
| Institut: | Professur Regelungstechnik und Systemdynamik | |
| Fakultät: | Fakultät für Elektrotechnik und Informationstechnik | |
| Dokumentart: | Habilitation | |
| DOI: | doi:10.60687/2025-0069 | |
| SWD-Schlagwörter: | Maschinelles Lernen , Optimierung , Regelungstechnik , Bestärkendes Lernen , Künstliche Intelligenz | |
| Freie Schlagwörter (Englisch): | Reinforcement learning , automatic control , optimal control , stability , safety , guarantees , formal analysis | |
| DDC-Sachgruppe: | 006.31, 006.32, 629.8, 004.21, 004.33, 005.1, 006.24 | |
| Sprache: | englisch | |
| Tag der mündlichen Prüfung | 14.04.2025 |
