… [Objective] This paper proposes an interpretable reinforcementlearning method for job … with the knowledge graph and the theory of reinforcementlearning. This algorithm, which divided …
ODVIADR LEARNING, Ö Ugur - 2022 - polen.itu.edu.tr
… This section provides a theoretical background on reinforcementlearning with a focus on deep Q-networks and their extensions. The proposed solution to our problem will be presented …
… It has been revealed that rewardshaping, which provides simpler and easier reward … reward, is an effective way to guide and accelerate reinforcementlearning. However, building a …
S Ayrom, M Tumer - The service industries journal, 2021 - Taylor & Francis
… Theoretical findings Our findings make empirical and theoretical contributions to the … Drawing upon social exchange theory, the findings suggest that although both IB and BOL …
F Wang, X Zhou - Victims & Offenders, 2023 - Taylor & Francis
… This study also emphasizes the importance of using empiricalresearch to address China’s detection, mitigation, and prevention of this type of scam. Finally, authorities and citizens …
… compass for navigating the uncharted waters of future research endeavors and the judicious deployment of multiagent reinforcementlearning algorithms in pragmatic scenarios. KEY …
… , and meta-game theory are introduced; opponent modeling methods that concludes the frontier theory of opponent modeling are summarized, and the applications and challenges …
HQ Zhang, Q Yan - Journal of China tourism research, 2009 - Taylor & Francis
… empiricalstudies can be carried out to achieve a comprehensive understanding of the mechanism of tourism policy making, particularly in the contemporary Chinese context, taking …