Voir la notice de l'article provenant de la source Math-Net.Ru
@article{IZKAB_2020_6_a1, author = {M. I. Anchekov}, title = {Applying reinforced learning for solving the problem of structuring the external environment}, journal = {News of the Kabardin-Balkar scientific center of RAS}, pages = {14--19}, publisher = {mathdoc}, number = {6}, year = {2020}, language = {ru}, url = {http://geodesic.mathdoc.fr/item/IZKAB_2020_6_a1/} }
TY - JOUR AU - M. I. Anchekov TI - Applying reinforced learning for solving the problem of structuring the external environment JO - News of the Kabardin-Balkar scientific center of RAS PY - 2020 SP - 14 EP - 19 IS - 6 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/IZKAB_2020_6_a1/ LA - ru ID - IZKAB_2020_6_a1 ER -
M. I. Anchekov. Applying reinforced learning for solving the problem of structuring the external environment. News of the Kabardin-Balkar scientific center of RAS, no. 6 (2020), pp. 14-19. http://geodesic.mathdoc.fr/item/IZKAB_2020_6_a1/
[1] A. Aydemir, P. Jensfelt, J. Folkesson, “What can we learn from 38,000 rooms? Reasoning about unexplored space in indoor environments”, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, 4675–4682 | DOI
[2] M. Luperto, F. Amigoni, “Predicting the global structure of indoor environments: A constructive machine learning approach”, Autonomous Robots, 43:4 (2018), 813–835 | DOI
[3] C. Galindo, A. Saffiotti, S. Coradeschi, P. Buschka, J. A. Fernandez-Madrigal , J. Gonzalez, “Multi-hierarchical semantic maps for mobile robotics”, IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alta, 2005, 2278–2283
[4] P. F. Patel-Schneider, A. L. Resnick, D. L. McGuinness, E. Weixelbaum, M. Abrahams, A. Borgida, NeoClassic Reference Manual: Version 1.0. AT Labs Research, Artificial Intelligence Principles Research Department, 1996
[5] M. I. Anchokov, M. P. Krivenko, “Computer model of the emergence of collective behavior of robots”, News of the KBSC of RAS, 2019, no. 6 (92), 21–26
[6] R. S. Sutton, E. G. Barto, Reinforcement learning, Binom. Knowledge Laboratory, 2017, 399 pp.