Marc G. Bellemare

According to our database, Marc G. Bellemare authored at least 76 papers between 2007 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Distributional Analogue to the Successor Representation.

Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland

CoRR, 2024

2023

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy.

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

CoRR, 2023

An Analysis of Quantile Temporal-Difference Learning.

Mark Rowland, Rémi Munos, Mohammad Gheshlaghi Azar, Yunhao Tang, Georg Ostrovski, Anna Harutyunyan, Karl Tuyls, Marc G. Bellemare, Will Dabney

CoRR, 2023

Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control.

Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Small batch deep reinforcement learning.

Johan S. Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Bigger, Better, Faster: Human-level Atari with human-level efficiency.

Max Schwarzer, Johan Samir Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro

Proceedings of the International Conference on Machine Learning, 2023

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation.

Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney

Proceedings of the International Conference on Machine Learning, 2023

Bootstrapped Representations in Reinforcement Learning.

Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney

Proceedings of the International Conference on Machine Learning, 2023

Investigating Multi-task Pretraining and Generalization in Reinforcement Learning.

Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare

Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning.

Johan Samir Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks.

Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier.

Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, Aaron C. Courville

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces.

Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Beyond Tabula Rasa: Reincarnating Reinforcement Learning.

Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare

CoRR, 2022

The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning.

Yunhao Tang, Rémi Munos, Mark Rowland, Bernardo Ávila Pires, Will Dabney, Marc G. Bellemare

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress.

Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning.

Harley E. Wiltzer, David Meger, Marc G. Bellemare

Proceedings of the International Conference on Machine Learning, 2022

On the Generalization of Representations in Reinforcement Learning.

Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Deep Reinforcement Learning at the Edge of the Statistical Precipice.

Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Importance of Pessimism in Fixed-Dataset Policy Optimization.

Jacob Buckman, Carles Gelada, Marc G. Bellemare

Proceedings of the 9th International Conference on Learning Representations, 2021

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning.

Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare

Proceedings of the 9th International Conference on Learning Representations, 2021

Metrics and Continuity in Reinforcement Learning.

Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning.

Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, David Silver

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Autonomous navigation of stratospheric balloons using reinforcement learning.

Marc G. Bellemare, Salvatore Candido, Pablo Samuel Castro, Jun Gong, Marlos C. Machado, Subhodeep Moitra, Sameera S. Ponda, Ziyu Wang

Nat., 2020

Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces.

Ahmed Touati, Adrien Ali Taïga, Marc G. Bellemare

CoRR, 2020

On Catastrophic Interference in Atari 2600 Games.

William Fedus, Dibya Ghosh, John D. Martin, Marc G. Bellemare, Yoshua Bengio, Hugo Larochelle

CoRR, 2020

The Hanabi challenge: A new frontier for AI research.

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

Artif. Intell., 2020

Representations for Stable Off-Policy Reinforcement Learning.

Dibya Ghosh, Marc G. Bellemare

Proceedings of the 37th International Conference on Machine Learning, 2020

On Bonus Based Exploration Methods In The Arcade Learning Environment.

Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare

Proceedings of the 8th International Conference on Learning Representations, 2020

Shaping the Narrative Arc: Information-Theoretic Collaborative DialoguePaper type: Technical Paper.

Kory Wallace Mathewson, Pablo Samuel Castro, Colin Cherry, George F. Foster, Marc G. Bellemare

Proceedings of the Eleventh International Conference on Computational Creativity, 2020

A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms.

Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Count-Based Exploration with the Successor Representation.

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

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction.

Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment.

Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron C. Courville, Marc G. Bellemare

CoRR, 2019

Hyperbolic Discounting and Learning over Multiple Horizons.

William Fedus, Carles Gelada, Yoshua Bengio, Marc G. Bellemare, Hugo Larochelle

CoRR, 2019

Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue.

Kory W. Mathewson, Pablo Samuel Castro, Colin Cherry, George F. Foster, Marc G. Bellemare

CoRR, 2019

A Geometric Perspective on Optimal Representations for Reinforcement Learning.

Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents.

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

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Statistics and Samples in Distributional Reinforcement Learning.

Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney

Proceedings of the 36th International Conference on Machine Learning, 2019

DeepMDP: Learning Continuous Latent Space Models for Representation Learning.

Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare

Proceedings of the 36th International Conference on Machine Learning, 2019

The Value Function Polytope in Reinforcement Learning.

Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans

Proceedings of the 36th International Conference on Machine Learning, 2019

Distributional reinforcement learning with linear function approximation.

Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

A Comparative Analysis of Expected and Distributional Reinforcement Learning.

Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift.

Carles Gelada, Marc G. Bellemare

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Temporally Extended Metrics for Markov Decision Processes.

Philip Amortila, Marc G. Bellemare, Prakash Panangaden, Doina Precup

Proceedings of the Workshop on Artificial Intelligence Safety 2019 co-located with the Thirty-Third AAAI Conference on Artificial Intelligence 2019 (AAAI-19), 2019

2018

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

Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, Michael Bowling

J. Artif. Intell. Res., 2018

An Introduction to Deep Reinforcement Learning.

Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau

Found. Trends Mach. Learn., 2018

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents.

Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Ludwig Schubert, Marc G. Bellemare, Jeff Clune, Joel Lehman

CoRR, 2018

Dopamine: A Research Framework for Deep Reinforcement Learning.

Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, Marc G. Bellemare

CoRR, 2018

The Barbados 2018 List of Open Issues in Continual Learning.

Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup

CoRR, 2018

Approximate Exploration through State Abstraction.

Adrien Ali Taïga, Aaron C. Courville, Marc G. Bellemare

CoRR, 2018

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (Extended Abstract).

Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J. Hausknecht, Michael Bowling

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning.

Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G. Bellemare, Rémi Munos

Proceedings of the 6th International Conference on Learning Representations, 2018

An Analysis of Categorical Distributional Reinforcement Learning.

Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Distributional Reinforcement Learning With Quantile Regression.

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

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

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

Audrunas Gruslys, Mohammad Gheshlaghi Azar, Marc G. Bellemare, Rémi Munos

CoRR, 2017

The Cramer Distance as a Solution to Biased Wasserstein Gradients.

Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos

CoRR, 2017

Count-Based Exploration with Neural Density Models.

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

Proceedings of the 34th International Conference on Machine Learning, 2017

A Laplacian Framework for Option Discovery in Reinforcement Learning.

Marlos C. Machado, Marc G. Bellemare, Michael H. Bowling

Proceedings of the 34th International Conference on Machine Learning, 2017

Automated Curriculum Learning for Neural Networks.

Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu

Proceedings of the 34th International Conference on Machine Learning, 2017

A Distributional Perspective on Reinforcement Learning.

Marc G. Bellemare, Will Dabney, Rémi Munos

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Safe and Efficient Off-Policy Reinforcement Learning.

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

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Unifying Count-Based Exploration and Intrinsic Motivation.

Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Q(λ) with Off-Policy Corrections.

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

Proceedings of the Algorithmic Learning Theory - 27th International Conference, 2016

Increasing the Action Gap: New Operators for Reinforcement Learning.

Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Human-level control through deep reinforcement learning.

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis

Nat., 2015

Online Learning of k-CNF Boolean Functions.

Joel Veness, Marcus Hutter, Laurent Orseau, Marc G. Bellemare

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract).

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

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Count-Based Frequency Estimation with Bounded Memory.

Marc G. Bellemare

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Compress and Control.

Joel Veness, Marc G. Bellemare, Marcus Hutter, Alvin Chua, Guillaume Desjardins

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Skip Context Tree Switching.

Marc G. Bellemare, Joel Veness, Erik Talvitie

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

The Arcade Learning Environment: An Evaluation Platform for General Agents.

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

J. Artif. Intell. Res., 2013

Bayesian Learning of Recursively Factored Environments.

Marc G. Bellemare, Joel Veness, Michael Bowling

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Sketch-Based Linear Value Function Approximation.

Marc G. Bellemare, Joel Veness, Michael Bowling

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Investigating Contingency Awareness Using Atari 2600 Games.

Marc G. Bellemare, Joel Veness, Michael Bowling

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2007

Context-Driven Predictions.

Marc G. Bellemare, Doina Precup

Proceedings of the IJCAI 2007, 2007