This program aims to reunite researchers across disciplines that have played a role in developing the theory of reinforcement learning. It will review past developments and identify promising directions of research, with an emphasis on addressing existing open problems, ranging from the design of efficient, scalable algorithms for exploration to how to control learning and planning. It also aims to deepen the understanding of model-free vs. model-based learning and control, and the design of efficient methods to exploit structure and adapt to easier environments.
TL;DR: Have you even wondered what is so special about convolution? In this post, I derive the convolution from first principles and show that it naturally emerges from translational symmetry. During…
R. Hanocka, G. Metzer, R. Giryes, and D. Cohen-Or. (2020)cite arxiv:2005.11084Comment: SIGGRAPH 2020; Project page: https://ranahanocka.github.io/point2mesh/.
H. Chawla, M. Jukola, T. Brouns, E. Arani, and B. Zonooz. (2020)cite arxiv:2007.12918Comment: Accepted at 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
H. Tajima, and F. Fujisawa. (2020)cite arxiv:2007.00926Comment: 6 pages, 5 figures, accepted by Scientific and Educational Reports of the Faculty of Science and Technology, Kochi University.