Matteo Hessel
Matteo Hessel
Research Engineer, Google DeepMind
Verified email at google.com
TitleCited byYear
Dueling Network Architectures for Deep Reinforcement Learning
Z Wang, T Schaul, M Hessel, H Van Hasselt, M Lanctot, N De Freitas
International Conference on Machine Learning (ICML 2016), 1995–2003, 2016
7582016
Rainbow: Combining Improvements in Deep Reinforcement Learning
M Hessel, J Modayil, H van Hasselt, T Schaul, G Ostrovski, W Dabney, ...
Association for the Advancement of Artificial Intelligence (AAAI 2018), 2017
3142017
Noisy networks for exploration
M Fortunato, MG Azar, B Piot, J Menick, M Hessel, I Osband, A Graves, ...
International Conference on Learning Representations (ICLR 2018), 2017
1712017
The predictron: End-to-end learning and planning
D Silver, H van Hasselt, M Hessel, T Schaul, A Guez, T Harley, ...
International Conference on Machine Learning (ICML 2017), 3191--3199, 2016
1052016
Distributed Prioritized Experience Replay
D Horgan, J Quan, D Budden, G Barth-Maron, M Hessel, H van Hasselt, ...
International Conference on Learning Representations (ICLR 2018), 2018
1002018
Learning values across many orders of magnitude
HP van Hasselt, A Guez, M Hessel, V Mnih, D Silver
Advances In Neural Information Processing Systems (NIPS 2016), 4287-4295, 2016
542016
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
322018
Multi-task Deep Reinforcement Learning with PopArt
M Hessel, H Soyer, L Espeholt, W Czarnecki, S Schmitt, H van Hasselt
Association for the Advancement of Artificial Intelligence (AAAI 2019), 2018
232018
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 (ICML 2018), 510-519, 2018
192018
Unicorn: Continual Learning with a Universal, Off-policy Agent
DJ Mankowitz, A Žídek, A Barreto, D Horgan, M Hessel, J Quan, J Oh, ...
Multi-disciplinary Conference on Reinforcement Learning and Decision Making …, 2018
122018
Deep Reinforcement Learning and the Deadly Triad
H van Hasselt, Y Doron, F Strub, M Hessel, N Sonnerat, J Modayil
Deep Reinforcement Learning Workshop (NeurIPS 2018), 2018
92018
Behaviour Suite for Reinforcement Learning
I Osband, Y Doron, M Hessel, J Aslanides, E Sezener, A Saraiva, ...
arXiv preprint arXiv:1908.03568, 2019
22019
On inductive biases in deep reinforcement learning
M Hessel, H van Hasselt, J Modayil, D Silver
Multi-disciplinary Conference on Reinforcement Learning and Decision Making …, 2019
22019
Automatic Tuning of Computational Models
M Hessel, F Ortalli, F Borgatelli, PL Lanzi
Simulation and Modeling Methodologies, Technologies and Applications, 43-64, 2015
2*2015
General non-linear Bellman equations
H van Hasselt, J Quan, M Hessel, Z Xu, D Borsa, A Barreto
Multi-disciplinary Conference on Reinforcement Learning and Decision Making …, 2019
12019
Off-Policy Actor-Critic with Shared Experience Replay
S Schmitt, M Hessel, K Simonyan
arXiv preprint arXiv:1909.11583, 2019
2019
Discovery of Useful Questions as Auxiliary Tasks
V Veeriah, M Hessel, Z Xu, R Lewis, J Rajendran, J Oh, H van Hasselt, ...
arXiv preprint arXiv:1909.04607, 2019
2019
Environment prediction using reinforcement learning
D Silver, T Schaul, M Hessel, HP van Hasselt
US Patent App. 16/403,314, 2019
2019
Continual reinforcement learning with a multi-task agent
T Schaul, M Hessel, HP Van Hasselt, DJ Mankowitz
US Patent App. 16/268,414, 2019
2019
When to use parametric models in reinforcement learning?
H van Hasselt, M Hessel, J Aslanides
arXiv preprint arXiv:1906.05243, 2019
2019
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Articles 1–20