Georg Ostrovski
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Geciteerd door
Human-level control through deep reinforcement learning
V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ...
nature 518 (7540), 529-533, 2015
Rainbow: Combining improvements in deep reinforcement learning
M Hessel, J Modayil, H Van Hasselt, T Schaul, G Ostrovski, W Dabney, ...
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
Hybrid computing using a neural network with dynamic external memory
A Graves, G Wayne, M Reynolds, T Harley, I Danihelka, ...
Nature 538 (7626), 471-476, 2016
Unifying count-based exploration and intrinsic motivation
M Bellemare, S Srinivasan, G Ostrovski, T Schaul, D Saxton, R Munos
Advances in neural information processing systems 29, 2016
Count-based exploration with neural density models
G Ostrovski, MG Bellemare, A Oord, R Munos
International conference on machine learning, 2721-2730, 2017
Implicit quantile networks for distributional reinforcement learning
W Dabney, G Ostrovski, D Silver, R Munos
International conference on machine learning, 1096-1105, 2018
Recurrent experience replay in distributed reinforcement learning
S Kapturowski, G Ostrovski, J Quan, R Munos, W Dabney
International conference on learning representations, 2018
Increasing the action gap: New operators for reinforcement learning
MG Bellemare, G Ostrovski, A Guez, P Thomas, R Munos
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
Temporally-extended {\epsilon}-greedy exploration
W Dabney, G Ostrovski, A Barreto
arXiv preprint arXiv:2006.01782, 2020
Autoregressive quantile networks for generative modeling
G Ostrovski, W Dabney, R Munos
International Conference on Machine Learning, 3936-3945, 2018
On the effect of auxiliary tasks on representation dynamics
C Lyle, M Rowland, G Ostrovski, W Dabney
International Conference on Artificial Intelligence and Statistics, 1-9, 2021
Symmetric decomposition of asymmetric games
K Tuyls, J Pérolat, M Lanctot, G Ostrovski, R Savani, JZ Leibo, T Ord, ...
Scientific reports 8 (1), 1015, 2018
The difficulty of passive learning in deep reinforcement learning
G Ostrovski, PS Castro, W Dabney
Advances in Neural Information Processing Systems 34, 23283-23295, 2021
Human-level control through deep reinforcement learning. nature, 518 (7540): 529–533, 2015
V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ...
Cited on 3 (4), 0
When should agents explore?
M Pislar, D Szepesvari, G Ostrovski, D Borsa, T Schaul
arXiv preprint arXiv:2108.11811, 2021
Payoff performance of fictitious play
G Ostrovski, S van Strien
Journal of Dynamics and Games 1 (4), 621-638, 2014
DQN Zoo: Reference implementations of DQN-based agents, 2020
J Quan, G Ostrovski
URL http://github. com/deepmind/dqn_zoo, 39, 0
Deep reinforcement learning with plasticity injection
E Nikishin, J Oh, G Ostrovski, C Lyle, R Pascanu, W Dabney, A Barreto
Advances in Neural Information Processing Systems 36, 2024
The phenomenon of policy churn
T Schaul, A Barreto, J Quan, G Ostrovski
Advances in Neural Information Processing Systems 35, 2537-2549, 2022
Adapting behaviour for learning progress
T Schaul, D Borsa, D Ding, D Szepesvari, G Ostrovski, W Dabney, ...
arXiv preprint arXiv:1912.06910, 2019
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Artikelen 1–20