Samuel Blad
Title: Doctoral Student School/office: School of Science and TechnologyEmail: samuel.blad@oru.se
Phone: +46 19 301076
Room: T2249
Research subject
Research environments
Research teams
Publications
Conference papers |
Conference papers
- Blad, S. , Längkvist, M. , Klügl, F. & Loutfi, A. (2022). Empirical analysis of the convergence of Double DQN in relation to reward sparsity. In: 2022 21st IEEE International Conference on Machine Learning and Applications Proceedings. Paper presented at 21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'22), Atlantis Hotel, Nassau, The Bahamas, Caribbean, December 12-14, 2022. IEEE.