Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data

Reinforcement learning from demonstrations (RLfD) is a promising approach to improve the exploration efficiency of reinforcement learning (RL) by learning from expert demonstrations in addition to interactions with the environment. In this paper, we propose a framework that combines techniques from...

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Veröffentlicht in:	Proceedings / International Conference on Software Engineering S. 38 - 50
Hauptverfasser:	Tappler, Martin, Pferscher, Andrea, Aichernig, Bernhard K., Konighofer, Bettina
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 14.04.2024
Schlagworte:	Deep reinforcement learning Fuzzing Maintenance engineering Policy repair Reinforcement learning from demonstrations Robustness Safety Search-based software testing Software reliability Training Video games
ISSN:	1558-1225
Online-Zugang:	Volltext
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