| Fast gradient-descent methods for temporal-difference learning with linear function approximation RS Sutton, HR Maei, D Precup, S Bhatnagar, D Silver, C Szepesvári, ... Proceedings of the 26th annual international conference on machine learning …, 2009 | 727 | 2009 |
| Convergent temporal-difference learning with arbitrary smooth function approximation HR Maei, S Szepesvári, Csaba, Bhatnagar, D Precup, D Silver, RS Sutton Advances in Neural Information Processing Systems, 1204-1212, 2009 | 350 | 2009 |
| Toward off-policy learning control with function approximation. HR Maei, C Szepesvári, S Bhatnagar, RS Sutton ICML 10, 719-726, 2010 | 342 | 2010 |
| Optimal demand response using device-based reinforcement learning Z Wen, D O’Neill, H Maei IEEE Transactions on Smart Grid 6 (5), 2312-2324, 2015 | 320 | 2015 |
| Involvement of the anterior cingulate cortex in the expression of remote spatial memory CM Teixeira, SR Pomedli, HR Maei, N Kee, PW Frankland Journal of Neuroscience 26 (29), 7555-7564, 2006 | 316 | 2006 |
| A Convergent Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation RS Sutton, H Maei, C Szepesvári Advances in neural information processing systems 21, 2008 | 277 | 2008 |
| What is the most sensitive measure of water maze probe test performance? HR Maei, K Zaslavsky, CM Teixeira, PW Frankland Frontiers in integrative neuroscience 3, 493, 2009 | 251 | 2009 |
| A convergent O (n) algorithm for off-policy temporal-difference learning with linear function approximation RS Sutton, C Szepesvári, HR Maei Advances in neural information processing systems 21 (21), 1609-1616, 2008 | 241 | 2008 |
| Gradient temporal-difference learning algorithms HR Maei | 210 | 2011 |
| GQ (lambda): A general gradient algorithm for temporal-difference prediction learning with eligibility traces HR Maei, RS Sutton 3d Conference on Artificial General Intelligence (AGI-2010), 100-105, 2010 | 163 | 2010 |
| Deep reinforcement learning for visual object tracking in videos D Zhang, H Maei, X Wang, YF Wang arXiv preprint arXiv:1701.08936, 2017 | 140 | 2017 |
| Randomly connected networks have short temporal memory E Wallace, HR Maei, PE Latham Neural computation 25 (6), 1408-1439, 2013 | 43 | 2013 |
| Convergent actor-critic algorithms under off-policy training and function approximation HR Maei arXiv preprint arXiv:1802.07842, 2018 | 38 | 2018 |
| Development and validation of a sensitive entropy-based measure for the water maze HR Maei, K Zaslavsky, AH Wang, AP Yiu, CM Teixeira, SA Josselyn, ... Frontiers in integrative neuroscience 3, 870, 2009 | 28 | 2009 |
| Correlated quantum percolation in the lowest Landau level N Sandler, HR Maei, J Kondev Physical Review B—Condensed Matter and Materials Physics 70 (4), 045309, 2004 | 26 | 2004 |
| A batch, off-policy, actor-critic algorithm for optimizing the average reward SA Murphy, Y Deng, EB Laber, HR Maei, RS Sutton, K Witkiewitz arXiv preprint arXiv:1607.05047, 2016 | 24 | 2016 |
| Quantum and classical localization in the lowest Landau level N Sandler, HR Maei, J Kondev Physical Review B 68 (20), 205315, 2003 | 14 | 2003 |
| How can realistic networks process time-varying signals? H Maei PQDT-Global, 2005 | 3 | 2005 |
| A novel analytic measure for the water maze utilizing the concept of entropy HR Lee, BK Kaang Frontiers in Neuroscience 4, 1825, 2010 | | 2010 |
| Convergent Temporal-Difference Learning with Arbitrary Differentiable Function Approximator HR Maei, C Szepesvári, S Bhathnagar, D Silver, D Precup, R Sutton | | 2010 |
Richard S. SuttonKeen, Amii, and University of AlbertaVerified email at richsutton.com
