Matteo Hessel

发表

Machine Learning for Parameter Screening in Computer Simulations

Matteo Hessel, Fabio Ortalli, Francesco Borgatelli, 2014, MESAS.

Off-Policy Actor-Critic with Shared Experience Replay

Karen Simonyan, Matteo Hessel, Simon Schmitt, 2020, ICML.

On Inductive Biases in Deep Reinforcement Learning pdf

David Silver, Matteo Hessel, Joseph Modayil, 2019, ArXiv.

Discovery of Useful Questions as Auxiliary Tasks

Richard L. Lewis, Junhyuk Oh, Satinder Singh, 2019, NeurIPS.

Unicorn: Continual Learning with a Universal, Off-policy Agent pdf

Tom Schaul, Junhyuk Oh, David Silver, 2018, ArXiv.

Automatic Tuning of Computational Models

Pier Luca Lanzi, Matteo Hessel, Fabio Ortalli, 2014, SIMULTECH.

A novel approach to model design and tuning through automatic parameter screening and optimization theory and application to a helicopter flight simulator case-study

Matteo Hessel, Fabio Ortalli, Francesco Borgatelli, 2014, 2014 4th International Conference On Simulation And Modeling Methodologies, Technologies And Applications (SIMULTECH).

What Can Learned Intrinsic Rewards Capture? pdf

Junhyuk Oh, Satinder Singh, David Silver, 2019, ICML.

Deep Reinforcement Learning and the Deadly Triad pdf

Matteo Hessel, Florian Strub, Joseph Modayil, 2018, ArXiv.

Self-Tuning Deep Reinforcement Learning pdf

Junhyuk Oh, Satinder Singh, David Silver, 2020, ArXiv.

Multi-task Deep Reinforcement Learning with PopArt

Wojciech Czarnecki, Matteo Hessel, Simon Schmitt, 2018, AAAI.

Expected Eligibility Traces pdf

David Silver, Matteo Hessel, Hado van Hasselt, 2020, ArXiv.

When to use parametric models in reinforcement learning?

Matteo Hessel, John Aslanides, Hado van Hasselt, 2019, NeurIPS.

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement pdf

Tom Schaul, Rémi Munos, David Silver, 2018, ICML.

Learning values across many orders of magnitude

David Silver, Matteo Hessel, Arthur Guez, 2016, NIPS.

Behaviour Suite for Reinforcement Learning pdf

Tor Lattimore, Richard S. Sutton, Csaba Szepesvári, 2019, ICLR.

Dueling Network Architectures for Deep Reinforcement Learning pdf

Tom Schaul, Nando de Freitas, Matteo Hessel, 2015, ICML.

The Predictron: End-To-End Learning and Planning pdf

Tom Schaul, David Silver, Matteo Hessel, 2016, ICML.

Meta-Gradient Reinforcement Learning with an Objective Discovered Online pdf

Junhyuk Oh, Satinder Singh, David Silver, 2020, NeurIPS.

Learning functions across many orders of magnitudes pdf

David Silver, Matteo Hessel, Arthur Guez, 2016, ArXiv.

General non-linear Bellman equations pdf

Matteo Hessel, Andre Barreto, Zhongwen Xu, 2019, ArXiv.

Observe and Look Further: Achieving Consistent Performance on Atari pdf

Rémi Munos, Olivier Pietquin, David Budden, 2018, ArXiv.

Distributed Prioritized Experience Replay pdf

David Budden, David Silver, Matteo Hessel, 2018, ICLR.

A Self-Tuning Actor-Critic Algorithm

Junhyuk Oh, Satinder Singh, David Silver, 2020, NeurIPS.

Rainbow: Combining Improvements in Deep Reinforcement Learning

Tom Schaul, David Silver, Matteo Hessel, 2017, AAAI.

Towards Consistent Performance on Atari using Expert Demonstrations

Rémi Munos, Olivier Pietquin, Matteo Hessel, 2018 .

Scaling shared model governance via model splitting pdf

Shane Legg, Pushmeet Kohli, Matteo Hessel, 2018, ArXiv.

Discovering Reinforcement Learning Algorithms pdf

Junhyuk Oh, Satinder Singh, David Silver, 2020, NeurIPS.

Muesli: Combining Improvements in Policy Optimization pdf

David Silver, Matteo Hessel, Fabio Viola, 2021, ICML.

Emphatic Algorithms for Deep Reinforcement Learning

Matteo Hessel, Charles Blundell, Adam White, 2021, ICML.

Discovery of Options via Meta-Learned Subgoals pdf

Junhyuk Oh, David Silver, Matteo Hessel, 2021, NeurIPS.

Podracer architectures for scalable Reinforcement Learning pdf

Matteo Hessel, Fabio Viola, Hado van Hasselt, 2021, ArXiv.

Self-Consistent Models and Values pdf

Zita Marinho, David Silver, Matteo Hessel, 2021, NeurIPS.