Daniel J. Mankowitz
Cited by
Cited by
Challenges of Real-World Reinforcement Learning
G Dulac-Arnold, DJ Mankowitz, T Hester
arXiv:1904.12901, 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
Reward constrained policy optimization
C Tessler, DJ Mankowitz, S Mannor
arXiv preprint arXiv:1805.11074, 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
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
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
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
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
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
Universal successor features approximators
D Borsa, A Barreto, J Quan, D Mankowitz, R Munos, H Van Hasselt, ...
arXiv preprint arXiv:1812.07626, 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
Adaptive Skills Adaptive Partitions (ASAP)
DJ Mankowitz, TA Mann, S Mannor
Neural Information Processing Systems (NIPS), Barcelona, Spain, 2016
Soft-robust actor-critic policy-gradient
E Derman, DJ Mankowitz, TA Mann, S Mannor
arXiv preprint arXiv:1803.04848, 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
Learning robust options
D Mankowitz, T Mann, PL Bacon, D Precup, S Mannor
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 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
A bayesian approach to robust reinforcement learning
E Derman, D Mankowitz, T Mann, S Mannor
Uncertainty in Artificial Intelligence, 648-658, 2020
Time-Regularized Interrupting Options (TRIO)
D Mankowitz, T Mann, S Mannor
Proceedings of the 31st International Conference on Machine Learning (ICML …, 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
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
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