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...

Full description

Bibliographic Details

Published in:	Applied sciences Vol. 12; no. 21; p. 10947
Main Authors:	Terra, Ahmad, Inam, Rafia, Fersman, Elena
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.11.2022
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!