STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration.
In: Applied Sciences (2076-3417), Jg. 14 (2024-04-01), Heft 7, S. 2916-2934
Online
academicJournal
Zugriff:
Experience-based methods like reinforcement learning (RL) are often deemed less suitable for the safety field due to concerns about potential safety issues. To bridge this gap, we introduce STPA-RL, a methodology that integrates RL with System-Theoretic Process Analysis (STPA). STPA is a safety analysis technique that identifies causative factors leading to unsafe control actions and system hazards through loss scenarios. In the context of STPA-RL, we formalize the Markov Decision Process based on STPA analysis results to incorporate control algorithms into the system environment. The agent learns safe actions through reward-based learning, tracking potential hazard paths to validate system safety. Specifically, by analyzing various loss scenarios related to the Platform Screen Door, we assess the applicability of the proposed approach by evaluating hazard trajectory graphs and hazard frequencies in the system. This paper streamlines the RL process for loss scenario identification through STPA, contributing to self-guided loss scenarios and diverse system modeling. Additionally, it offers effective simulations for proactive development to enhance system safety and provide practical assistance in the safety field. [ABSTRACT FROM AUTHOR]
Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration.
|
---|---|
Autor/in / Beteiligte Person: | Chang, Jiyoung ; Kwon, Ryeonggu ; Kwon, Gihwon |
Link: | |
Zeitschrift: | Applied Sciences (2076-3417), Jg. 14 (2024-04-01), Heft 7, S. 2916-2934 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 2076-3417 (print) |
DOI: | 10.3390/app14072916 |
Schlagwort: |
|
Sonstiges: |
|