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Samuel Blad

Samuel Blad Title: Doctoral Student School/office: School of Science and Technology

Email: samuel.blad@oru.se

Phone: +46 19 301076

Room: T2249

Samuel Blad
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Research subject
  • Computer Science
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  • Centre for Applied Autonomous Sensor Systems (AASS)
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  • Machine Perception and Interaction Lab

Publications

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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.
  • Updated: 2023-01-03
  • Editor: Samuel Blad
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