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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Veröffentlicht in:	Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) S. 213 - 217
1. Verfasser:	Yerushalmi, Raz
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.05.2023
Schlagworte:	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-Zugang:	Volltext
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