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Samuel Blad

Position: Doctoral Student School/office: School of Science and Technology

Email: c2FtdWVsLmJsYWQ7b3J1LnNl

Phone: No number available

Room: T2215

Samuel Blad
Research subject Research environments

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: Wani, MA; Kantardzic, M; Palade, V; Neagu, D; Yang, L; Chan, KY,  21st IEEE International Conference on Machine Learning and Applications. ICMLA 2022 Proceedings. Paper presented at 21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), Nassau, Bahamas, December 12-14, 2022. (pp. 591-596). IEEE. [BibTeX]