BEERL: Both Ends Explanations for Reinforcement Learning

Deep Reinforcement Learning (RL) is a black-box method and is hard to understand because the agent employs a neural network (NN). To explain the behavior and decisions made by the agent, different eXplainable RL (XRL) methods are developed; for example, feature importance methods are applied to anal...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Applied sciences Jg. 12; H. 21; S. 10947
Hauptverfasser:	Terra, Ahmad, Inam, Rafia, Fersman, Elena
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.11.2022
Schlagworte:	Artificial intelligence Bias Decomposition deep reinforcement learning explainability explainable reinforcement learning Methods Neural networks Performance evaluation reward decomposition reward prioritization Variables
ISSN:	2076-3417, 2076-3417
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!