Marc G. Bellemare

ORCID

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 110

Top Venues

Publications

Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
A Distributional Analogue to the Successor Representation. CoRR (2024)
Nate Rahn, Pierluca D'Oro, Marc G. Bellemare
Controlling Large Language Model Agents with Entropic Activation Steering. CoRR (2024)
Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, Aaron C. Courville
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. ICLR (2023)
Johan S. Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro
Small batch deep reinforcement learning. NeurIPS (2023)
Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. CoRR (2023)
Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. CoRR (2023)
Max Schwarzer, Jesse Farebrother, Joshua Greaves, Ekin Dogus Cubuk, Rishabh Agarwal, Aaron C. Courville, Marc G. Bellemare, Sergei V. Kalinin, Igor Mordatch, Pablo Samuel Castro, Kevin M. Roccapriore
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy. CoRR (2023)
Johan Samir Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro
The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning. Tiny Papers @ ICLR (2023)
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
Bootstrapped Representations in Reinforcement Learning. CoRR (2023)
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
Bootstrapped Representations in Reinforcement Learning. ICML (2023)
Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. ICML (2023)
Max Schwarzer, Johan S. Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro
Bigger, Better, Faster: Human-level Atari with human-level efficiency. CoRR (2023)
Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control. NeurIPS (2023)
Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. AISTATS (2023)
Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare
Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. ICLR (2023)
Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney
An Analysis of Quantile Temporal-Difference Learning. CoRR (2023)
Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control. CoRR (2023)
Johan S. Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro
Small batch deep reinforcement learning. CoRR (2023)
Max Schwarzer, Johan Samir Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro
Bigger, Better, Faster: Human-level Atari with human-level efficiency. ICML (2023)
Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. ICLR (2023)
Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. NeurIPS (2022)
Harley E. Wiltzer, David Meger, Marc G. Bellemare
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning. ICML (2022)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. NeurIPS (2022)
Harley Wiltzer, David Meger, Marc G. Bellemare
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning. CoRR (2022)
Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare
On the Generalization of Representations in Reinforcement Learning. AISTATS (2022)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
Beyond Tabula Rasa: Reincarnating Reinforcement Learning. CoRR (2022)
Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare
On the Generalization of Representations in Reinforcement Learning. CoRR (2022)
Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. CoRR (2022)
Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. CoRR (2022)
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. CoRR (2021)
Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. AAAI (2021)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
Deep Reinforcement Learning at the Edge of the Statistical Precipice. CoRR (2021)
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare
Deep Reinforcement Learning at the Edge of the Statistical Precipice. NeurIPS (2021)
Jacob Buckman, Carles Gelada, Marc G. Bellemare
The Importance of Pessimism in Fixed-Dataset Policy Optimization. ICLR (2021)
Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. ICLR (2021)
Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro
Metrics and continuity in reinforcement learning. CoRR (2021)
Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro
Metrics and Continuity in Reinforcement Learning. AAAI (2021)
Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare
On Bonus-Based Exploration Methods in the Arcade Learning Environment. CoRR (2021)
Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. AISTATS (2020)
Marlos C. Machado, Marc G. Bellemare, Michael Bowling
Count-Based Exploration with the Successor Representation. AAAI (2020)
William Fedus, Dibya Ghosh, John D. Martin, Marc G. Bellemare, Yoshua Bengio, Hugo Larochelle
On Catastrophic Interference in Atari 2600 Games. CoRR (2020)
Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare
Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction. AAAI (2020)
Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. CoRR (2020)
Ahmed Touati, Adrien Ali Taïga, Marc G. Bellemare
Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces. CoRR (2020)
Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare
On Bonus Based Exploration Methods In The Arcade Learning Environment. ICLR (2020)
Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling
The Hanabi challenge: A new frontier for AI research. Artif. Intell. 280 (2020)
Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. CoRR (2020)
Marc G. Bellemare, Salvatore Candido, Pablo Samuel Castro, Jun Gong, Marlos C. Machado, Subhodeep Moitra, Sameera S. Ponda, Ziyu Wang
Autonomous navigation of stratospheric balloons using reinforcement learning. Nat. 588 (7836) (2020)
Dibya Ghosh, Marc G. Bellemare
Representations for Stable Off-Policy Reinforcement Learning. CoRR (2020)
Kory Wallace Mathewson, Pablo Samuel Castro, Colin Cherry, George F. Foster, Marc G. Bellemare
Shaping the Narrative Arc: Information-Theoretic Collaborative DialoguePaper type: Technical Paper. ICCC (2020)
Jacob Buckman, Carles Gelada, Marc G. Bellemare
The Importance of Pessimism in Fixed-Dataset Policy Optimization. CoRR (2020)
Dibya Ghosh, Marc G. Bellemare
Representations for Stable Off-Policy Reinforcement Learning. ICML (2020)
Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling
The Hanabi Challenge: A New Frontier for AI Research. CoRR (2019)
Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction. CoRR (2019)
Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans
The Value Function Polytope in Reinforcement Learning. ICML (2019)
William Fedus, Carles Gelada, Yoshua Bengio, Marc G. Bellemare, Hugo Larochelle
Hyperbolic Discounting and Learning over Multiple Horizons. CoRR (2019)
Carles Gelada, Marc G. Bellemare
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift. CoRR (2019)
Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare
DeepMDP: Learning Continuous Latent Space Models for Representation Learning. ICML (2019)
Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare
DeepMDP: Learning Continuous Latent Space Models for Representation Learning. CoRR (2019)
Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment. CoRR (2019)
Philip Amortila, Marc G. Bellemare, Prakash Panangaden, Doina Precup
Temporally Extended Metrics for Markov Decision Processes. SafeAI@AAAI (2019)
Carles Gelada, Marc G. Bellemare
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift. AAAI (2019)
Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra
Distributional reinforcement learning with linear function approximation. AISTATS (2019)
Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
Statistics and Samples in Distributional Reinforcement Learning. ICML (2019)
Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
Statistics and Samples in Distributional Reinforcement Learning. CoRR (2019)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS (2019)
Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare
A Comparative Analysis of Expected and Distributional Reinforcement Learning. CoRR (2019)
Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare
The Value Function Polytope in Reinforcement Learning. CoRR (2019)
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
A Geometric Perspective on Optimal Representations for Reinforcement Learning. CoRR (2019)
Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Jiale Zhi, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents. IJCAI (2019)
Kory W. Mathewson, Pablo Samuel Castro, Colin Cherry, George F. Foster, Marc G. Bellemare
Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue. CoRR (2019)
Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra
Distributional reinforcement learning with linear function approximation. CoRR (2019)
Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro
A Comparative Analysis of Expected and Distributional Reinforcement Learning. AAAI (2019)
Adrien Ali Taïga, Aaron C. Courville, Marc G. Bellemare
Approximate Exploration through State Abstraction. CoRR (2018)
Marlos C. Machado, Marc G. Bellemare, Michael Bowling
Count-Based Exploration with the Successor Representation. CoRR (2018)
Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup
The Barbados 2018 List of Open Issues in Continual Learning. CoRR (2018)
Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh
An Analysis of Categorical Distributional Reinforcement Learning. AISTATS (2018)
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, Marc G. Bellemare
Dopamine: A Research Framework for Deep Reinforcement Learning. CoRR (2018)
Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents. CoRR (2018)
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, Michael Bowling
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents. J. Artif. Intell. Res. 61 (2018)
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, Michael Bowling
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (Extended Abstract). IJCAI (2018)
Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau
An Introduction to Deep Reinforcement Learning. Found. Trends Mach. Learn. 11 (3-4) (2018)
Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
Distributional Reinforcement Learning With Quantile Regression. AAAI (2018)
Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G. Bellemare, Rémi Munos
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning. ICLR (Poster) (2018)
Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau
An Introduction to Deep Reinforcement Learning. CoRR (2018)
Georg Ostrovski, Marc G. Bellemare, Aäron van den Oord, Rémi Munos
Count-Based Exploration with Neural Density Models. CoRR (2017)
Marlos C. Machado, Marc G. Bellemare, Michael H. Bowling
A Laplacian Framework for Option Discovery in Reinforcement Learning. CoRR (2017)
Marc G. Bellemare, Will Dabney, Rémi Munos
A Distributional Perspective on Reinforcement Learning. ICML (2017)
Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos
The Cramer Distance as a Solution to Biased Wasserstein Gradients. CoRR (2017)
Marc G. Bellemare, Will Dabney, Rémi Munos
A Distributional Perspective on Reinforcement Learning. CoRR (2017)
Marlos C. Machado, Marc G. Bellemare, Michael H. Bowling
A Laplacian Framework for Option Discovery in Reinforcement Learning. ICML (2017)
Audrunas Gruslys, Mohammad Gheshlaghi Azar, Marc G. Bellemare, Rémi Munos
The Reactor: A Sample-Efficient Actor-Critic Architecture. CoRR (2017)
Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu
Automated Curriculum Learning for Neural Networks. ICML (2017)
Georg Ostrovski, Marc G. Bellemare, Aäron van den Oord, Rémi Munos
Count-Based Exploration with Neural Density Models. ICML (2017)
Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu
Automated Curriculum Learning for Neural Networks. CoRR (2017)
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, Michael Bowling
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents. CoRR (2017)
Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
Distributional Reinforcement Learning with Quantile Regression. CoRR (2017)
Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos
Increasing the Action Gap: New Operators for Reinforcement Learning. AAAI (2016)
Anna Harutyunyan, Marc G. Bellemare, Tom Stepleton, Rémi Munos
Q(λ) with Off-Policy Corrections. ALT (2016)
Rémi Munos, Tom Stepleton, Anna Harutyunyan, Marc G. Bellemare
Safe and Efficient Off-Policy Reinforcement Learning. CoRR (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Marc G. Bellemare