TY - GEN
T1 - Using Deep Reinforcement Learning and Formal Verification in Safety Critical Systems
T2 - 23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023
AU - Sharma, Satyam
AU - Rahim, Muhammad Abdul Basit Ur
AU - Hussain, Shahid
AU - Abid, Muhammad Rizwan
AU - Liu, Tairan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Deep Reinforcement Learning (DRL) is critical in modern Artificial Intelligence (AI), powering innovations from gaming to autonomous vehicles. As DRL continues its rapid ascent, ensuring its systems are both trustworthy and effective is crucial. This research focuses on different DRL techniques and the challenges faced in real-life scenarios. The paper also describes various formal verification techniques and the challenges related to their application. It sheds light on the different frameworks and tools that can enhance the credibility of systems. We performed an extensive literature survey to present the existing methodologies, tools, and frameworks. The analysis systematically reviews and categorizes various formal verification techniques and frameworks employed in DRL. The insights garnered from this study are anticipated to foster an enriched understanding of the processes and contribute to decision-making in Safety Critical Systems using DRL and verification.
AB - Deep Reinforcement Learning (DRL) is critical in modern Artificial Intelligence (AI), powering innovations from gaming to autonomous vehicles. As DRL continues its rapid ascent, ensuring its systems are both trustworthy and effective is crucial. This research focuses on different DRL techniques and the challenges faced in real-life scenarios. The paper also describes various formal verification techniques and the challenges related to their application. It sheds light on the different frameworks and tools that can enhance the credibility of systems. We performed an extensive literature survey to present the existing methodologies, tools, and frameworks. The analysis systematically reviews and categorizes various formal verification techniques and frameworks employed in DRL. The insights garnered from this study are anticipated to foster an enriched understanding of the processes and contribute to decision-making in Safety Critical Systems using DRL and verification.
UR - http://www.scopus.com/inward/record.url?scp=85186745071&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85186745071&partnerID=8YFLogxK
U2 - 10.1109/QRS-C60940.2023.00112
DO - 10.1109/QRS-C60940.2023.00112
M3 - Conference contribution
AN - SCOPUS:85186745071
T3 - Proceedings - 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023
SP - 834
EP - 842
BT - Proceedings - 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 October 2023 through 26 October 2023
ER -