| Challenges of Real-World Reinforcement Learning G Dulac-Arnold, DJ Mankowitz, T Hester arXiv:1904.12901, 2019 | 543 | 2019 |
| 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 | 401 | 2017 |
| Reward constrained policy optimization C Tessler, DJ Mankowitz, S Mannor arXiv preprint arXiv:1805.11074, 2018 | 396 | 2018 |
| Competition-level code generation with alphacode Y Li, D Choi, J Chung, N Kushman, J Schrittwieser, R Leblond, T Eccles, ... Science 378 (6624), 1092-1097, 2022 | 337 | 2022 |
| Challenges of real-world reinforcement learning: definitions, benchmarks and analysis G Dulac-Arnold, N Levine, DJ Mankowitz, J Li, C Paduraru, S Gowal, ... Machine Learning 110 (9), 2419-2468, 2021 | 241 | 2021 |
| 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 31, 2018 | 214 | 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 | 160 | 2018 |
| Rl unplugged: Benchmarks for offline reinforcement learning C Gulcehre, Z Wang, A Novikov, T Le Paine, SG Colmenarejo, K Zolna, ... arXiv preprint arXiv:2006.13888 394, 2020 | 142* | 2020 |
| 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 | 97 | 2020 |
| Universal successor features approximators D Borsa, A Barreto, J Quan, D Mankowitz, R Munos, H Van Hasselt, ... arXiv preprint arXiv:1812.07626, 2018 | 96 | 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 | 93 | 2019 |
| Adaptive Skills Adaptive Partitions (ASAP) DJ Mankowitz, TA Mann, S Mannor Neural Information Processing Systems (NIPS), Barcelona, Spain, 2016 | 67 | 2016 |
| Soft-robust actor-critic policy-gradient E Derman, DJ Mankowitz, TA Mann, S Mannor arXiv preprint arXiv:1803.04848, 2018 | 50 | 2018 |
| Shallow updates for deep reinforcement learning N Levine, T Zahavy, DJ Mankowitz, A Tamar, S Mannor Advances in Neural Information Processing Systems 30, 2017 | 48 | 2017 |
| Learning robust options D Mankowitz, T Mann, PL Bacon, D Precup, S Mannor Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 44 | 2018 |
| 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 | 43 | 2018 |
| A bayesian approach to robust reinforcement learning E Derman, D Mankowitz, T Mann, S Mannor Uncertainty in Artificial Intelligence, 648-658, 2020 | 41 | 2020 |
| Time-Regularized Interrupting Options (TRIO) D Mankowitz, T Mann, S Mannor Proceedings of the 31st International Conference on Machine Learning (ICML …, 2014 | 40* | 2014 |
| Muzero with self-competition for rate control in vp9 video compression A Mandhane, A Zhernov, M Rauh, C Gu, M Wang, F Xue, W Shang, ... arXiv preprint arXiv:2202.06626, 2022 | 19 | 2022 |
| Discovering a set of policies for the worst case reward T Zahavy, A Barreto, DJ Mankowitz, S Hou, B O'Donoghue, I Kemaev, ... arXiv preprint arXiv:2102.04323, 2021 | 19 | 2021 |
Shie MannorProfessor of Electrical Engineering @ Technion & Researcher @ Nvidia ResearchVerified email at technion.ac.il
