Enhancing Deep Reinforcement Learning with Executable Specifications

Deep reinforcement learning (DRL) has become a dominant paradigm for using deep learning to carry out tasks where complex policies are learned for reactive systems. However, these policies are "black-boxes", e.g., opaque to humans and known to be susceptible to bugs. For example, it is har...

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Bibliographic Details

Published in:	Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) pp. 213 - 217
Main Author:	Yerushalmi, Raz
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.05.2023
Subjects:	Deep learning deep reinforcement learning domain expertise Machine learning Medical services Reinforcement learning rule-based specifications Safety Scalability scenario-based modeling Task analysis Training
ISSN:	2574-1934
Online Access:	Get full text
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