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DeepMind
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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
1502018
Universal successor features approximators
D Borsa, A Barreto, J Quan, D Mankowitz, R Munos, H Van Hasselt, ...
arXiv preprint arXiv:1812.07626, 2018
902018
Fast reinforcement learning with generalized policy updates
A Barreto, S Hou, D Borsa, D Silver, D Precup
Proceedings of the National Academy of Sciences 117 (48), 30079-30087, 2020
812020
Detecting disease outbreaks in mass gatherings using Internet data
E Yom-Tov, D Borsa, IJ Cox, RA McKendry
Journal of medical Internet research 16 (6), e3156, 2014
712014
The option keyboard: Combining skills in reinforcement learning
A Barreto, D Borsa, S Hou, G Comanici, E Aygün, P Hamel, D Toyama, ...
Advances in Neural Information Processing Systems 32, 2019
692019
Ray interference: a source of plateaus in deep reinforcement learning
T Schaul, D Borsa, J Modayil, R Pascanu
arXiv preprint arXiv:1904.11455, 2019
552019
Observational learning by reinforcement learning
D Borsa, B Piot, R Munos, O Pietquin
arXiv preprint arXiv:1706.06617, 2017
512017
The termination critic
A Harutyunyan, W Dabney, D Borsa, N Heess, R Munos, D Precup
arXiv preprint arXiv:1902.09996, 2019
452019
Learning shared representations in multi-task reinforcement learning
D Borsa, T Graepel, J Shawe-Taylor
arXiv preprint arXiv:1603.02041, 2016
342016
Expected eligibility traces
H van Hasselt, S Madjiheurem, M Hessel, D Silver, A Barreto, D Borsa
Proceedings of the AAAI Conference on Artificial Intelligence 35 (11), 9997 …, 2021
312021
Automatic identification of web-based risk markers for health events
E Yom-Tov, D Borsa, AC Hayward, RA McKendry, IJ Cox
Journal of medical Internet research 17 (1), e4082, 2015
312015
Training deep neural nets to aggregate crowdsourced responses
A Gaunt, D Borsa, Y Bachrach
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial …, 2016
292016
When should agents explore?
M Pîslar, D Szepesvari, G Ostrovski, D Borsa, T Schaul
arXiv preprint arXiv:2108.11811, 2021
122021
Adapting behaviour for learning progress
T Schaul, D Borsa, D Ding, D Szepesvari, G Ostrovski, W Dabney, ...
arXiv preprint arXiv:1912.06910, 2019
122019
General non-linear bellman equations
H van Hasselt, J Quan, M Hessel, Z Xu, D Borsa, A Barreto
arXiv preprint arXiv:1907.03687, 2019
122019
Conditional importance sampling for off-policy learning
M Rowland, A Harutyunyan, H Hasselt, D Borsa, T Schaul, R Munos, ...
International Conference on Artificial Intelligence and Statistics, 45-55, 2020
112020
Temporal difference uncertainties as a signal for exploration
S Flennerhag, JX Wang, P Sprechmann, F Visin, A Galashov, ...
arXiv preprint arXiv:2010.02255, 2020
102020
Return-based scaling: Yet another normalisation trick for deep RL
T Schaul, G Ostrovski, I Kemaev, D Borsa
arXiv preprint arXiv:2105.05347, 2021
62021
Model-value inconsistency as a signal for epistemic uncertainty
A Filos, E Vértes, Z Marinho, G Farquhar, D Borsa, A Friesen, ...
arXiv preprint arXiv:2112.04153, 2021
32021
The wreath process: A totally generative model of geometric shape based on nested symmetries
D Borsa, T Graepel, A Gordon
arXiv preprint arXiv:1506.03041, 2015
32015
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