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John Quan
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2020 – today
- 2023
- [i13]Kate Baumli, Satinder Baveja, Feryal M. P. Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Hussain Masoom, Kay McKinney, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei Zhang:
Vision-Language Models as a Source of Rewards. CoRR abs/2312.09187 (2023) - 2022
- [c10]Tom Schaul, André Barreto, John Quan, Georg Ostrovski:
The Phenomenon of Policy Churn. NeurIPS 2022 - [i12]Tom Schaul, André Barreto, John Quan, Georg Ostrovski:
The Phenomenon of Policy Churn. CoRR abs/2206.00730 (2022) - 2021
- [c9]Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver:
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. AAAI 2021: 7160-7168 - [i11]Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, Hado van Hasselt:
Podracer architectures for scalable Reinforcement Learning. CoRR abs/2104.06272 (2021) - 2020
- [i10]Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver:
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. CoRR abs/2006.02243 (2020)
2010 – 2019
- 2019
- [c8]Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul:
Universal Successor Features Approximators. ICLR (Poster) 2019 - [c7]Steven Kapturowski, Georg Ostrovski, John Quan, Rémi Munos, Will Dabney:
Recurrent Experience Replay in Distributed Reinforcement Learning. ICLR (Poster) 2019 - [i9]André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos:
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. CoRR abs/1901.10964 (2019) - [i8]Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, André Barreto:
General non-linear Bellman equations. CoRR abs/1907.03687 (2019) - 2018
- [c6]Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys:
Deep Q-learning From Demonstrations. AAAI 2018: 3223-3230 - [c5]Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver:
Distributed Prioritized Experience Replay. ICLR (Poster) 2018 - [c4]André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos:
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. ICML 2018: 510-519 - [i7]Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul:
Unicorn: Continual Learning with a Universal, Off-policy Agent. CoRR abs/1802.08294 (2018) - [i6]Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver:
Distributed Prioritized Experience Replay. CoRR abs/1803.00933 (2018) - [i5]Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Vecerík, Matteo Hessel, Rémi Munos, Olivier Pietquin:
Observe and Look Further: Achieving Consistent Performance on Atari. CoRR abs/1805.11593 (2018) - [i4]Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul:
Universal Successor Features Approximators. CoRR abs/1812.07626 (2018) - 2017
- [c3]Yee Whye Teh, Victor Bapst, Wojciech M. Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu:
Distral: Robust multitask reinforcement learning. NIPS 2017: 4496-4506 - [i3]Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu:
Distral: Robust Multitask Reinforcement Learning. CoRR abs/1707.04175 (2017) - [i2]Oriol Vinyals, Timo Ewalds
, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John P. Agapiou
StarCraft II: A New Challenge for Reinforcement Learning. CoRR abs/1708.04782 (2017) - 2016
- [c2]Tom Schaul, John Quan, Ioannis Antonoglou, David Silver:
Prioritized Experience Replay. ICLR (Poster) 2016 - [i1]James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell:
Overcoming catastrophic forgetting in neural networks. CoRR abs/1612.00796 (2016) - 2011
- [j1]John Quan, Kara L. Nance, Brian Hay:
A Mutualistic Security Service Model: Supporting Large-Scale Virtualized Environments. IT Prof. 13(3): 18-23 (2011) - [c1]Donald Kline, John Quan:
Attribute Description Service for Large-Scale Networks. HCI (16) 2011: 519-528
Coauthor Index
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