Black-Box Hyperparameter Tuning for Risk-Constrained Reinforcement Learning Algorithm – Bc. Ján Petrák
Bc. Ján Petrák
Master's thesis
Black-Box Hyperparameter Tuning for Risk-Constrained Reinforcement Learning Algorithm
Black-Box Hyperparameter Tuning for Risk-Constrained Reinforcement Learning Algorithm
Abstract:
Táto práca poskytuje prehľad novodobých prístupov k optimalizácii hyperparametrov, ako aj prehľad plánovacieho algoritmu s obmedzeným riskom RAlph, vrátane jeho veľkého množstva hyperparameterov. Práca pokračuje uvedením nevyhnutných súčastí potrebných pre optimalizáciu RAlpových hyperparametrov, ako je napríklad definíca cieľovej funkcie. Po krátkom popise implementácie, práca prezentuje výsledky …moreAbstract:
The thesis summarizes modern approaches to hyperparameter tuning, followed by a presentation of a risk-constrained reinforcement learning algorithm RAlph, including its wide range of hyperparameters that substantially influence its performance. The continuation of the thesis comprises the necessary setup for tuning RAlph's hyperparameters, such as the definition of an appropriate objective function …more
Language used: English
Date on which the thesis was submitted / produced: 14. 12. 2021
Identifier:
https://is.muni.cz/th/j90iu/
Thesis defence
- Date of defence: 3. 2. 2022
- Supervisor: RNDr. Petr Novotný, Ph.D.
- Reader: doc. RNDr. Tomáš Brázdil, Ph.D.
Citation record
ISO 690-compliant citation record:
PETRÁK, Ján. \textit{Black-Box Hyperparameter Tuning for Risk-Constrained Reinforcement Learning Algorithm}. Online. Master's thesis. Brno: Masaryk University, Faculty of Informatics. 2021. Available from: https://theses.cz/id/asr4c8/.
Full text of thesis
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Published in Theses:- světu
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Institution archiving the thesis and making it accessible: Masarykova univerzita, Fakulta informatikyMasaryk University
Faculty of InformaticsMaster programme / field:
Computer systems, communication and security / Networks and communication
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