Using Deep Reinforcement Learning And Formal Verification in Safety Critical Systems: Strategies and Challenges

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...

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Bibliographic Details
Main Authors: Sharma, Satyam, Rahim, Muhammad Abdul Basit Ur, Hussain, Shahid, Abid, Muhammad Rizwan, Liu, Tairan
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Decision making
Deep learning
Deep Reinforcement Learning
Formal Methods
Formal verification
Industries
Reinforcement learning
Safety
Safety Critical Systems
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Summary: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.
ISSN:2693-9371
DOI:10.1109/QRS-C60940.2023.00112