Alexander Pritzel
Alexander Pritzel
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Cited by
Cited by
Continuous control with deep reinforcement learning
TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ...
arXiv preprint arXiv:1509.02971, 2015
Simple and scalable predictive uncertainty estimation using deep ensembles
B Lakshminarayanan, A Pritzel, C Blundell
Advances in neural information processing systems, 6402-6413, 2017
Deep exploration via bootstrapped DQN
I Osband, C Blundell, A Pritzel, B Van Roy
Advances in neural information processing systems, 4026-4034, 2016
Pathnet: Evolution channels gradient descent in super neural networks
C Fernando, D Banarse, C Blundell, Y Zwols, D Ha, AA Rusu, A Pritzel, ...
arXiv preprint arXiv:1701.08734, 2017
Vector-based navigation using grid-like representations in artificial agents
A Banino, C Barry, B Uria, C Blundell, T Lillicrap, P Mirowski, A Pritzel, ...
Nature 557 (7705), 429-433, 2018
Darla: Improving zero-shot transfer in reinforcement learning
I Higgins, A Pal, AA Rusu, L Matthey, CP Burgess, A Pritzel, M Botvinick, ...
arXiv preprint arXiv:1707.08475, 2017
Neural episodic control
A Pritzel, B Uria, S Srinivasan, A Puigdomenech, O Vinyals, D Hassabis, ...
arXiv preprint arXiv:1703.01988, 2017
Model-free episodic control
C Blundell, B Uria, A Pritzel, Y Li, A Ruderman, JZ Leibo, J Rae, ...
arXiv preprint arXiv:1606.04460, 2016
Scrambling in the black hole portrait
G Dvali, D Flassig, C Gomez, A Pritzel, N Wintergerst
Physical Review D 88 (12), 124041, 2013
Black holes and quantumness on macroscopic scales
D Flassig, A Pritzel, N Wintergerst
Physical Review D 87 (8), 084007, 2013
Memory-based parameter adaptation
P Sprechmann, SM Jayakumar, JW Rae, A Pritzel, AP Badia, B Uria, ...
arXiv preprint arXiv:1802.10542, 2018
On ghosts in theories of self-interacting massive spin-2 particles
S Folkerts, A Pritzel, N Wintergerst
arXiv preprint arXiv:1107.3157, 2011
Meta-learning by the baldwin effect
C Fernando, J Sygnowski, S Osindero, J Wang, T Schaul, D Teplyashin, ...
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2018
Topological model for domain walls in (super-) Yang-Mills theories
M Dierigl, A Pritzel
Physical Review D 90 (10), 105008, 2014
Never Give Up: Learning Directed Exploration Strategies
AP Badia, P Sprechmann, A Vitvitskyi, D Guo, B Piot, S Kapturowski, ...
arXiv preprint arXiv:2002.06038, 2020
Generative temporal models with spatial memory for partially observed environments
M Fraccaro, DJ Rezende, Y Zwols, A Pritzel, SM Eslami, F Viola
arXiv preprint arXiv:1804.09401, 2018
Fast deep reinforcement learning using online adjustments from the past
S Hansen, A Pritzel, P Sprechmann, A Barreto, C Blundell
Advances in Neural Information Processing Systems, 10567-10577, 2018
Large-N ground state of the Lieb-Liniger model and Yang-Mills theory on a two-sphere
D Flassig, A Franca, A Pritzel
Physical Review A 93 (1), 013627, 2016
Continuous control with deep reinforcement learning
Y Bengio, TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, D Wierstra
Found. Trends R Mach. Learn 2, 1-127, 2009
Localization of gauge fields and Maxwell-Chern-Simons theory
D Flassig, A Pritzel
Physical Review D 84 (12), 125024, 2011
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