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Computer Science

D-Index
33
Citations
44108
World Ranking
12334
National Ranking
4988

Overview

Marc G. Bellemare is affiliated with Google in the United States and has contributed extensively to the field of computer science, with a primary focus on artificial intelligence. Their work spans significant subfields such as artificial intelligence, management science and operations research, structural biology, surfaces, coatings and films, and computational theory and mathematics.

The research topics covered by Marc G. Bellemare include:

  • Reinforcement Learning in Robotics
  • Evolutionary Algorithms and Applications
  • Advanced Bandit Algorithms Research
  • Artificial Intelligence in Games
  • Advanced Electron Microscopy Techniques and Applications
  • Electron and X-Ray Spectroscopy Techniques
  • Digital Games and Media

Notable published papers by Marc G. Bellemare are:

  • Autonomous navigation of stratospheric balloons using reinforcement learning (2020, Nature)
  • Investigating Contingency Awareness Using Atari 2600 Games (2021, Proceedings of the AAAI Conference on Artificial Intelligence)

Frequent co-authors include:

  • Pablo Samuel Castro (13 joint publications)
  • Rishabh Agarwal (10 joint publications)
  • Will Dabney (9 joint publications)
  • Aaron Courville (7 joint publications)
  • Joshua Greaves (7 joint publications)

Marc G. Bellemare has published predominantly in venues such as:

  • arXiv (Cornell University) with 28 publications
  • Proceedings of the AAAI Conference on Artificial Intelligence with 5 publications
  • Microscopy and Microanalysis with 2 publications
  • Nature with 1 publication
  • Advanced Materials Interfaces with 1 publication

In addition to journal and conference papers, Marc G. Bellemare has contributed to book publications, including a title published by The MIT Press:

  • Distributional Reinforcement Learning (2023)

Best Publications

  • Human-level control through deep reinforcement learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu

  • The arcade learning environment: an evaluation platform for general agents

    Marc G. Bellemare;Yavar Naddaf;Joel Veness;Michael Bowling

  • An Introduction to Deep Reinforcement Learning

    Vincent François-Lavet;Peter Henderson;Riashat Islam;Marc G. Bellemare

  • Unifying count-based exploration and intrinsic motivation

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

  • A Distributional Perspective on Reinforcement Learning

    Marc G. Bellemare;Will Dabney;Rémi Munos

  • Distributional Reinforcement Learning With Quantile Regression

    Will Dabney;Mark Rowland;Marc G. Bellemare;Rémi Munos

  • Count-based exploration with neural density models

    Georg Ostrovski;Marc G. Bellemare;Aäron van den Oord;Rémi Munos

  • Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

    Marlos C. Machado;Marc G. Bellemare;Erik Talvitie;Joel Veness

  • Safe and Efficient Off-Policy Reinforcement Learning

    Rémi Munos;Tom Stepleton;Anna Harutyunyan;Marc G. Bellemare

  • Automated Curriculum Learning for Neural Networks

    Alex Graves;Marc G. Bellemare;Jacob Menick;Rémi Munos

  • The Cramer Distance as a Solution to Biased Wasserstein Gradients

    Marc G. Bellemare;Ivo Danihelka;Will Dabney;Shakir Mohamed

  • Autonomous navigation of stratospheric balloons using reinforcement learning.

    Marc G. Bellemare;Salvatore Candido;Pablo Samuel Castro;Jun Gong

  • The Hanabi Challenge: A New Frontier for AI Research

    Nolan Bard;Jakob N. Foerster;Sarath Chandar;Neil Burch

  • Dopamine: A Research Framework for Deep Reinforcement Learning

    Pablo Samuel Castro;Subhodeep Moitra;Carles Gelada;Saurabh Kumar

  • A Laplacian Framework for option discovery in reinforcement learning

    Marlos C. Machado;Marc G. Bellemare;Michael Bowling

  • Increasing the action gap: new operators for reinforcement learning

    Marc G. Bellemare;Georg Ostrovski;Arthur Guez;Philip S. Thomas

  • DeepMDP: Learning Continuous Latent Space Models for Representation Learning

    Carles Gelada;Saurabh Kumar;Jacob Buckman;Ofir Nachum

  • Count-Based Exploration with the Successor Representation

    Marlos C. Machado;Marc G. Bellemare;Michael Bowling

  • Investigating contingency awareness using Atari 2600 games

    Marc G. Bellemare;Joel Veness;Michael Bowling

  • The Reactor: A Sample-Efficient Actor-Critic Architecture

    Audrunas Gruslys;Mohammad Gheshlaghi Azar;Marc G. Bellemare;Remi Munos

  • Hyperbolic Discounting and Learning over Multiple Horizons

    William Fedus;Carles Gelada;Yoshua Bengio;Marc G. Bellemare

Frequent Co-Authors

Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Michael Bowling
Michael Bowling University of Alberta
Doina Precup
Doina Precup McGill University
Aaron Courville
Aaron Courville University of Montreal
Hugo Larochelle
Hugo Larochelle Google (United States)
Dale Schuurmans
Dale Schuurmans University of Alberta
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Marcus Hutter
Marcus Hutter DeepMind (United Kingdom)
Joelle Pineau
Joelle Pineau McGill University
Tom Schaul
Tom Schaul DeepMind (United Kingdom)

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