| A deep hierarchical approach to lifelong learning in minecraft C Tessler, S Givony, T Zahavy, DJ Mankowitz, S Mannor In Proc. Association for the Advancement of Artificial Intelligence (AAAI), 2017 | 231 | 2017 |
| Challenges of real-world reinforcement learning G Dulac-Arnold, D Mankowitz, T Hester arXiv preprint arXiv:1904.12901, 2019 | 94 | 2019 |
| Learn what not to learn: Action elimination with deep reinforcement learning T Zahavy, M Haroush, N Merlis, DJ Mankowitz, S Mannor Advances in Neural Information Processing Systems, 3562-3573, 2018 | 71* | 2018 |
| Reward constrained policy optimization C Tessler, DJ Mankowitz, S Mannor arXiv preprint arXiv:1805.11074, 2018 | 62 | 2018 |
| Transfer in deep reinforcement learning using successor features and generalised policy improvement A Barreto, D Borsa, J Quan, T Schaul, D Silver, M Hessel, D Mankowitz, ... International Conference on Machine Learning, 501-510, 2018 | 52 | 2018 |
| Adaptive Skills Adaptive Partitions (ASAP) DJ Mankowitz, TA Mann, S Mannor Neural Information Processing Systems (NIPS), Barcelona, Spain, 2016 | 41 | 2016 |
| Time-Regularized Interrupting Options (TRIO) D Mankowitz, T Mann, S Mannor Proceedings of the 31st International Conference on Machine Learning (ICML …, 2014 | 38* | 2014 |
| Shallow updates for deep reinforcement learning N Levine, T Zahavy, DJ Mankowitz, A Tamar, S Mannor Advances in Neural Information Processing Systems, 3135-3145, 2017 | 28 | 2017 |
| Unicorn: Continual learning with a universal, off-policy agent DJ Mankowitz, A Žídek, A Barreto, D Horgan, M Hessel, J Quan, J Oh, ... arXiv preprint arXiv:1802.08294, 2018 | 23 | 2018 |
| Universal successor features approximators D Borsa, A Barreto, J Quan, D Mankowitz, R Munos, H van Hasselt, ... arXiv preprint arXiv:1812.07626, 2018 | 18 | 2018 |
| Learning robust options DJ Mankowitz, TA Mann, PL Bacon, D Precup, S Mannor arXiv preprint arXiv:1802.03236, 2018 | 18 | 2018 |
| Soft-robust actor-critic policy-gradient E Derman, DJ Mankowitz, TA Mann, S Mannor arXiv preprint arXiv:1803.04848, 2018 | 14 | 2018 |
| Robust reinforcement learning for continuous control with model misspecification DJ Mankowitz, N Levine, R Jeong, Y Shi, J Kay, A Abdolmaleki, ... arXiv preprint arXiv:1906.07516, 2019 | 12 | 2019 |
| Mobile device-based cellular network coverage analysis using crowd sourcing JD Mankowitz, AJ Paverd 2011 IEEE EUROCON-International Conference on Computer as a Tool, 1-6, 2011 | 10 | 2011 |
| An empirical investigation of the challenges of real-world reinforcement learning G Dulac-Arnold, N Levine, DJ Mankowitz, J Li, C Paduraru, S Gowal, ... arXiv preprint arXiv:2003.11881, 2020 | 8 | 2020 |
| BRISK-based visual feature extraction for resource constrained robots DJ Mankowitz, S Ramamoorthy Robot Soccer World Cup, 195-206, 2013 | 7 | 2013 |
| Iterative hierarchical optimization for misspecified problems (IHOMP) DJ Mankowitz, TA Mann, S Mannor arXiv preprint arXiv:1602.03348, 2016 | 6 | 2016 |
| A bayesian approach to robust reinforcement learning E Derman, D Mankowitz, T Mann, S Mannor Uncertainty in Artificial Intelligence, 648-658, 2020 | 5 | 2020 |
| Action assembly: Sparse imitation learning for text based games with combinatorial action spaces C Tessler, T Zahavy, D Cohen, DJ Mankowitz, S Mannor arXiv preprint arXiv:1905.09700, 2019 | 5 | 2019 |
| CFORB: Circular FREAK-ORB Visual Odometry DJ Mankowitz, E Rivlin arXiv preprint arXiv:1506.05257, 2015 | 4 | 2015 |
Shie MannorProfessor of Electrical Engineering, TechnionVerified email at technion.ac.il
