Discrete Event Modeling and Simulation for Reinforcement Learning System Design
Discrete event modeling and simulation and reinforcement learning are two frameworks suited for cyberphysical system design, which, when combined, can give powerful tools for system optimization or decision making process for example. This paper describes how discrete event modeling and simulation c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/13/3/121 |
| _version_ | 1797470673603395584 |
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| author | Laurent Capocchi Jean-François Santucci |
| author_facet | Laurent Capocchi Jean-François Santucci |
| author_sort | Laurent Capocchi |
| collection | DOAJ |
| description | Discrete event modeling and simulation and reinforcement learning are two frameworks suited for cyberphysical system design, which, when combined, can give powerful tools for system optimization or decision making process for example. This paper describes how discrete event modeling and simulation could be integrated into reinforcement learning concepts and tools in order to assist in the realization of reinforcement learning systems, more specially considering the temporal, hierarchical, and multi-agent aspects. An overview of these different improvements are given based on the implementation of the Q-Learning reinforcement learning algorithm in the framework of the Discrete Event system Specification (DEVS) and System Entity Structure (SES) formalisms. |
| first_indexed | 2024-03-09T19:40:24Z |
| format | Article |
| id | doaj.art-4a398a1b99914f3b90b7326f502cc347 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T19:40:24Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-4a398a1b99914f3b90b7326f502cc3472023-11-24T01:41:33ZengMDPI AGInformation2078-24892022-02-0113312110.3390/info13030121Discrete Event Modeling and Simulation for Reinforcement Learning System DesignLaurent Capocchi0Jean-François Santucci1SPE UMR CNRS 6134, University of Corsica, 20250 Corte, FranceSPE UMR CNRS 6134, University of Corsica, 20250 Corte, FranceDiscrete event modeling and simulation and reinforcement learning are two frameworks suited for cyberphysical system design, which, when combined, can give powerful tools for system optimization or decision making process for example. This paper describes how discrete event modeling and simulation could be integrated into reinforcement learning concepts and tools in order to assist in the realization of reinforcement learning systems, more specially considering the temporal, hierarchical, and multi-agent aspects. An overview of these different improvements are given based on the implementation of the Q-Learning reinforcement learning algorithm in the framework of the Discrete Event system Specification (DEVS) and System Entity Structure (SES) formalisms.https://www.mdpi.com/2078-2489/13/3/121modelingsimulationmachine learningreinforcement learning |
| spellingShingle | Laurent Capocchi Jean-François Santucci Discrete Event Modeling and Simulation for Reinforcement Learning System Design Information modeling simulation machine learning reinforcement learning |
| title | Discrete Event Modeling and Simulation for Reinforcement Learning System Design |
| title_full | Discrete Event Modeling and Simulation for Reinforcement Learning System Design |
| title_fullStr | Discrete Event Modeling and Simulation for Reinforcement Learning System Design |
| title_full_unstemmed | Discrete Event Modeling and Simulation for Reinforcement Learning System Design |
| title_short | Discrete Event Modeling and Simulation for Reinforcement Learning System Design |
| title_sort | discrete event modeling and simulation for reinforcement learning system design |
| topic | modeling simulation machine learning reinforcement learning |
| url | https://www.mdpi.com/2078-2489/13/3/121 |
| work_keys_str_mv | AT laurentcapocchi discreteeventmodelingandsimulationforreinforcementlearningsystemdesign AT jeanfrancoissantucci discreteeventmodelingandsimulationforreinforcementlearningsystemdesign |