Matteo Hessel
Matteo Hessel
Research Engineer, Google DeepMind
Verified email at google.com
Title
Cited by
Cited by
Year
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
14062016
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
6952017
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
3392017
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
2422018
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
1622016
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
952016
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
622018
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
482018
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
422018
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
402018
Behaviour Suite for Reinforcement Learning
I Osband, Y Doron, M Hessel, J Aslanides, E Sezener, A Saraiva, ...
arXiv preprint arXiv:1908.03568, 2019
262019
When to use parametric models in reinforcement learning?
HP van Hasselt, M Hessel, J Aslanides
Advances in Neural Information Processing Systems, 14322-14333, 2019
262019
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
232018
Discovery of useful questions as auxiliary tasks
V Veeriah, M Hessel, Z Xu, J Rajendran, RL Lewis, J Oh, HP van Hasselt, ...
Advances in Neural Information Processing Systems, 9310-9321, 2019
162019
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
82019
Self-Tuning Deep Reinforcement Learning
T Zahavy, Z Xu, V Veeriah, M Hessel, J Oh, H van Hasselt, D Silver, ...
arXiv preprint arXiv:2002.12928, 2020
52020
Off-Policy Actor-Critic with Shared Experience Replay
S Schmitt, M Hessel, K Simonyan
arXiv preprint arXiv:1909.11583, 2019
52019
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
42019
What Can Learned Intrinsic Rewards Capture?
Z Zheng, J Oh, M Hessel, Z Xu, M Kroiss, H van Hasselt, D Silver, S Singh
arXiv preprint arXiv:1912.05500, 2019
32019
Discovering Reinforcement Learning Algorithms
J Oh, M Hessel, WM Czarnecki, Z Xu, H van Hasselt, S Singh, D Silver
arXiv preprint arXiv:2007.08794, 2020
22020
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Articles 1–20