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Computer Science

D-Index
42
Citations
26838
World Ranking
8131
National Ranking
486

Overview

Tom Schaul is affiliated with DeepMind in the United Kingdom. Their research primarily spans the field of computer science, with a strong focus on artificial intelligence. The subfields of study in which they have contributed include artificial intelligence, computational theory and mathematics, safety research, modeling and simulation, and mechanical engineering.

Schaul's research topics cover a range of areas within artificial intelligence and machine learning. Key topics they have worked on include:

  • Reinforcement Learning in Robotics
  • Evolutionary Algorithms and Applications
  • Metaheuristic Optimization Algorithms Research
  • Explainable Artificial Intelligence (XAI)
  • Advanced Multi-Objective Optimization Algorithms
  • Machine Learning and Algorithms
  • Artificial Intelligence in Games

Their publications have appeared in a variety of venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • Nature Communications
  • Proceedings of the Genetic and Evolutionary Computation Conference
  • QUT ePrints (Queensland University of Technology)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)

Notable recent papers by Tom Schaul include:

  • "AI for social good: unlocking the opportunity for positive impact" (2020), published in Nature Communications
  • "Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization" (2023), published in Proceedings of the Genetic and Evolutionary Computation Conference
  • "Policy Evaluation Networks" (2020), published on arXiv (Cornell University)
  • "Artificial curiosity for autonomous space exploration" (2022), published in QUT ePrints (Queensland University of Technology)
  • "Return-based Scaling: Yet Another Normalisation Trick for Deep RL" (2021), published on arXiv (Cornell University)

Schaul frequently collaborates with several researchers including Sebastian Flennerhag, Robert Tjarko Lange, Yutian Chen, Tom Zahavy, and Doina Precup.

Best Publications

  • Grandmaster level in StarCraft II using multi-agent reinforcement learning.

    Oriol Vinyals;Igor Babuschkin;Wojciech M. Czarnecki;Michaël Mathieu

  • Prioritized Experience Replay

    Tom Schaul;John Quan;Ioannis Antonoglou;David Silver

  • Dueling network architectures for deep reinforcement learning

    Ziyu Wang;Tom Schaul;Matteo Hessel;Hado Van Hasselt

  • Rainbow: Combining Improvements in Deep Reinforcement Learning

    Matteo Hessel;Joseph Modayil;Hado van Hasselt;Tom Schaul

  • Learning to learn by gradient descent by gradient descent

    Marcin Andrychowicz;Misha Denil;Sergio Gomez;Matthew W. Hoffman

  • Unifying count-based exploration and intrinsic motivation

    Marc G. Bellemare;Sriram Srinivasan;Georg Ostrovski;Tom Schaul

  • Reinforcement Learning with Unsupervised Auxiliary Tasks

    Max Jaderberg;Volodymyr Mnih;Wojciech Marian Czarnecki;Tom Schaul

  • StarCraft II: A New Challenge for Reinforcement Learning

    Oriol Vinyals;Timo Ewalds;Sergey Bartunov;Petko Georgiev

  • Universal Value Function Approximators

    Tom Schaul;Daniel Horgan;Karol Gregor;David Silver

  • FeUdal Networks for Hierarchical Reinforcement Learning

    Alexander Sasha Vezhnevets;Simon Osindero;Tom Schaul;Nicolas Heess

  • Natural Evolution Strategies

    D. Wierstra;T. Schaul;J. Peters;J. Schmidhuber

  • Natural evolution strategies

    Daan Wierstra;Tom Schaul;Tobias Glasmachers;Yi Sun

  • Deep Q-learning from Demonstrations

    Todd Hester;Matej Vecerik;Olivier Pietquin;Marc Lanctot

  • Deep Q-learning From Demonstrations.

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • No more pesky learning rates

    Tom Schaul;Sixin Zhang;Yann LeCun

  • Successor Features for Transfer in Reinforcement Learning

    Andre Barreto;Will Dabney;Remi Munos;Jonathan J. Hunt

  • AI for social good: unlocking the opportunity for positive impact.

    Nenad Tomašev;Julien Cornebise;Frank Hutter;Frank Hutter;Shakir Mohamed

  • The 2014 General Video Game Playing Competition

    Diego Perez-Liebana;Spyridon Samothrakis;Julian Togelius;Tom Schaul

  • A video game description language for model-based or interactive learning

    Tom Schaul

  • Exponential natural evolution strategies

    Tobias Glasmachers;Tom Schaul;Sun Yi;Daan Wierstra

  • The predictron: end-to-end learning and planning

    David Silver;Hado van Hasselt;Matteo Hessel;Tom Schaul

Frequent Co-Authors

David Silver
David Silver DeepMind (United Kingdom)
Jürgen Schmidhuber
Jürgen Schmidhuber King Abdullah University of Science and Technology
Hado van Hasselt
Hado van Hasselt University College London
Daan Wierstra
Daan Wierstra DeepMind (United Kingdom)
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Julian Togelius
Julian Togelius New York University
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)

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