Safety Constraint-Guided Reinforcement Learning with Linear Temporal Logic
In the context of reinforcement learning (RL), ensuring both safety and performance is crucial, especially in real-world scenarios where mistakes can lead to severe consequences. This study aims to address this challenge by integrating temporal logic constraints into RL algorithms, thereby providing...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/11/11/535 |
| _version_ | 1827638685296754688 |
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| author | Ryeonggu Kwon Gihwon Kwon |
| author_facet | Ryeonggu Kwon Gihwon Kwon |
| author_sort | Ryeonggu Kwon |
| collection | DOAJ |
| description | In the context of reinforcement learning (RL), ensuring both safety and performance is crucial, especially in real-world scenarios where mistakes can lead to severe consequences. This study aims to address this challenge by integrating temporal logic constraints into RL algorithms, thereby providing a formal mechanism for safety verification. We employ a combination of theoretical and empirical methods, including the use of temporal logic for formal verification and extensive simulations to validate our approach. Our results demonstrate that the proposed method not only maintains high levels of safety but also achieves comparable performance to traditional RL algorithms. Importantly, our approach fills a critical gap in the existing literature by offering a solution that is both mathematically rigorous and empirically validated. The study concludes that the integration of temporal logic into RL offers a promising avenue for developing algorithms that are both safe and efficient. This work lays the foundation for future research aimed at generalizing this approach to various complex systems and applications. |
| first_indexed | 2024-03-09T16:24:28Z |
| format | Article |
| id | doaj.art-4a1c31913096407e9b0d93cb9aa50ba6 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-09T16:24:28Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-4a1c31913096407e9b0d93cb9aa50ba62023-11-24T15:09:07ZengMDPI AGSystems2079-89542023-11-01111153510.3390/systems11110535Safety Constraint-Guided Reinforcement Learning with Linear Temporal LogicRyeonggu Kwon0Gihwon Kwon1Department of Computer Science, Kyonggi University, Gwanggyosan-ro, Yeongtong-gu, Suwon-si 154-42, Gyeonggi-do, Republic of KoreaDepartment of Computer Science, Kyonggi University, Gwanggyosan-ro, Yeongtong-gu, Suwon-si 154-42, Gyeonggi-do, Republic of KoreaIn the context of reinforcement learning (RL), ensuring both safety and performance is crucial, especially in real-world scenarios where mistakes can lead to severe consequences. This study aims to address this challenge by integrating temporal logic constraints into RL algorithms, thereby providing a formal mechanism for safety verification. We employ a combination of theoretical and empirical methods, including the use of temporal logic for formal verification and extensive simulations to validate our approach. Our results demonstrate that the proposed method not only maintains high levels of safety but also achieves comparable performance to traditional RL algorithms. Importantly, our approach fills a critical gap in the existing literature by offering a solution that is both mathematically rigorous and empirically validated. The study concludes that the integration of temporal logic into RL offers a promising avenue for developing algorithms that are both safe and efficient. This work lays the foundation for future research aimed at generalizing this approach to various complex systems and applications.https://www.mdpi.com/2079-8954/11/11/535RLsafety constraintlinear temporal logicformal verification |
| spellingShingle | Ryeonggu Kwon Gihwon Kwon Safety Constraint-Guided Reinforcement Learning with Linear Temporal Logic Systems RL safety constraint linear temporal logic formal verification |
| title | Safety Constraint-Guided Reinforcement Learning with Linear Temporal Logic |
| title_full | Safety Constraint-Guided Reinforcement Learning with Linear Temporal Logic |
| title_fullStr | Safety Constraint-Guided Reinforcement Learning with Linear Temporal Logic |
| title_full_unstemmed | Safety Constraint-Guided Reinforcement Learning with Linear Temporal Logic |
| title_short | Safety Constraint-Guided Reinforcement Learning with Linear Temporal Logic |
| title_sort | safety constraint guided reinforcement learning with linear temporal logic |
| topic | RL safety constraint linear temporal logic formal verification |
| url | https://www.mdpi.com/2079-8954/11/11/535 |
| work_keys_str_mv | AT ryeonggukwon safetyconstraintguidedreinforcementlearningwithlineartemporallogic AT gihwonkwon safetyconstraintguidedreinforcementlearningwithlineartemporallogic |