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Doina Precup
Doina Precup
DeepMind and McGill University
Bestätigte E-Mail-Adresse bei cs.mcgill.ca
Titel
Zitiert von
Zitiert von
Jahr
The multimodal brain tumor image segmentation benchmark (BRATS)
BH Menze, A Jakab, S Bauer, J Kalpathy-Cramer, K Farahani, J Kirby, ...
IEEE transactions on medical imaging 34 (10), 1993-2024, 2014
49942014
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
RS Sutton, D Precup, S Singh
Artificial intelligence 112 (1-2), 181-211, 1999
40981999
Deep reinforcement learning that matters
P Henderson, R Islam, P Bachman, J Pineau, D Precup, D Meger
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
19902018
Off-policy deep reinforcement learning without exploration
S Fujimoto, D Meger, D Precup
International conference on machine learning, 2052-2062, 2019
11632019
The option-critic architecture
PL Bacon, J Harb, D Precup
Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017
11032017
Eligibility traces for off-policy policy evaluation
D Precup
Computer Science Department Faculty Publication Series, 80, 2000
8592000
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
6722009
Learning with pseudo-ensembles
P Bachman, O Alsharif, D Precup
Advances in neural information processing systems 27, 2014
5592014
Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction
RS Sutton, J Modayil, M Delp, T Degris, PM Pilarski, A White, D Precup
The 10th International Conference on Autonomous Agents and Multiagent …, 2011
5532011
Algorithms for multi-armed bandit problems
V Kuleshov, D Precup
arXiv preprint arXiv:1402.6028, 2014
4992014
Reward is enough
D Silver, S Singh, D Precup, RS Sutton
Artificial Intelligence 299, 103535, 2021
4372021
Off-policy temporal-difference learning with function approximation
D Precup, RS Sutton, S Dasgupta
ICML, 417-424, 2001
4352001
Learning options in reinforcement learning
M Stolle, D Precup
Abstraction, Reformulation, and Approximation: 5th International Symposium …, 2002
4302002
Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation
T Nair, D Precup, DL Arnold, T Arbel
Medical image analysis 59, 101557, 2020
4052020
Temporal abstraction in reinforcement learning
D Precup
University of Massachusetts Amherst, 2000
3742000
Metrics for finite Markov decision processes
N Ferns, P Panangaden, D Precup
arXiv preprint arXiv:1207.4114, 2012
3172012
Convergent temporal-difference learning with arbitrary smooth function approximation
H Maei, C Szepesvari, S Bhatnagar, D Precup, D Silver, RS Sutton
Advances in neural information processing systems 22, 2009
3142009
Conditional computation in neural networks for faster models
E Bengio, PL Bacon, J Pineau, D Precup
arXiv preprint arXiv:1511.06297, 2015
2992015
Reproducibility of benchmarked deep reinforcement learning tasks for continuous control
R Islam, P Henderson, M Gomrokchi, D Precup
arXiv preprint arXiv:1708.04133, 2017
2852017
Intra-Option Learning about Temporally Abstract Actions.
RS Sutton, D Precup, S Singh
ICML 98, 556-564, 1998
2151998
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