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...

Celý popis

Podrobná bibliografia

Vydané v:	Proceedings / International Conference on Software Engineering s. 38 - 50
Hlavní autori:	Tappler, Martin, Pferscher, Andrea, Aichernig, Bernhard K., Konighofer, Bettina
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	ACM 14.04.2024
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!