John Quan
John Quan
Google DeepMind
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Cited by
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Overcoming catastrophic forgetting in neural networks
J Kirkpatrick, R Pascanu, N Rabinowitz, J Veness, G Desjardins, AA Rusu, ...
Proceedings of the national academy of sciences 114 (13), 3521-3526, 2017
Prioritized experience replay
T Schaul, J Quan, I Antonoglou, D Silver
International Conference on Learning Representations (ICLR), 2016
Deep q-learning from demonstrations
T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ...
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
Starcraft ii: A new challenge for reinforcement learning
O Vinyals, T Ewalds, S Bartunov, P Georgiev, AS Vezhnevets, M Yeo, ...
arXiv preprint arXiv:1708.04782, 2017
Distributed prioritized experience replay
D Horgan, J Quan, D Budden, G Barth-Maron, M Hessel, H Van Hasselt, ...
arXiv preprint arXiv:1803.00933, 2018
Distral: Robust multitask reinforcement learning
Y Teh, V Bapst, WM Czarnecki, J Quan, J Kirkpatrick, R Hadsell, N Heess, ...
Advances in neural information processing systems 30, 2017
Recurrent experience replay in distributed reinforcement learning
S Kapturowski, G Ostrovski, J Quan, R Munos, W Dabney
International conference on learning representations, 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
The DeepMind JAX Ecosystem
IB DeepMind, K Baumli, A Bell, S Bhupatiraju, J Bruce, P Buchlovsky, ...
URL, 2020
Observe and look further: Achieving consistent performance on atari
T Pohlen, B Piot, T Hester, MG Azar, D Horgan, D Budden, G Barth-Maron, ...
arXiv preprint arXiv:1805.11593, 2018
Universal successor features approximators
D Borsa, A Barreto, J Quan, D Mankowitz, R Munos, H Van Hasselt, ...
arXiv preprint arXiv:1812.07626, 2018
The value-improvement path: Towards better representations for reinforcement learning
W Dabney, A Barreto, M Rowland, R Dadashi, J Quan, MG Bellemare, ...
Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 7160-7168, 2021
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
Podracer architectures for scalable reinforcement learning
M Hessel, M Kroiss, A Clark, I Kemaev, J Quan, T Keck, F Viola, ...
arXiv preprint arXiv:2104.06272, 2021
DQN Zoo: Reference implementations of DQN-based agents
J Quan, G Ostrovski
URL, 2020
Training neural networks using a prioritized experience memory
T Schaul, J Quan, D Silver
US Patent 10,650,310, 2020
Reply to Huszár: The elastic weight consolidation penalty is empirically valid
J Kirkpatrick, R Pascanu, N Rabinowitz, J Veness, G Desjardins, AA Rusu, ...
Proceedings of the National Academy of Sciences 115 (11), E2498-E2498, 2018
The phenomenon of policy churn
T Schaul, A Barreto, J Quan, G Ostrovski
Advances in Neural Information Processing Systems 35, 2537-2549, 2022
General non-linear bellman equations
H van Hasselt, J Quan, M Hessel, Z Xu, D Borsa, A Barreto
arXiv preprint arXiv:1907.03687, 2019
Reinforcement learning using distributed prioritized replay
D Budden, G Barth-Maron, J Quan, DG Horgan
US Patent 11,625,604, 2023
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