Bellemare, Marc G.

59 publications

NeurIPS 2025 Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning Yash Jhaveri, Harley Wiltzer, Patrick Shafto, Marc G Bellemare, David Meger

NeurIPS 2025 Tapered Off-Policy REINFORCE - Stable and Efficient Reinforcement Learning for Large Language Models Nicolas Le Roux, Marc G Bellemare, Jonathan Lebensold, Arnaud Bergeron, Joshua Greaves, Alexandre Fréchette, Carolyne Pelletier, Eric Thibodeau-Laufer, Sándor Tóth, Sam Work

ICML 2024 A Distributional Analogue to the Successor Representation Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, Andre Barreto, Will Dabney, Marc G Bellemare, Mark Rowland

NeurIPS 2024 Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning Harley Wiltzer, Marc G. Bellemare, David Meger, Patrick Shafto, Yash Jhaveri

JMLR 2024 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

ICMLW 2024 Controlling Large Language Model Agents with Entropic Activation Steering Nate Rahn, Pierluca D'Oro, Marc G Bellemare

AISTATS 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

ICML 2023 Bigger, Better, Faster: Human-Level Atari with Human-Level Efficiency Max Schwarzer, Johan Samir Obando Ceron, Aaron Courville, Marc G Bellemare, Rishabh Agarwal, Pablo Samuel Castro

ICML 2023 Bootstrapped Representations in Reinforcement Learning Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G Bellemare, Will Dabney

ICLRW 2023 Bootstrapped Representations in Reinforcement Learning Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G Bellemare, Will Dabney

ICLR 2023 Investigating Multi-Task Pretraining and Generalization in Reinforcement Learning Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, Marc G Bellemare

ICLR 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

ICLR 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 Courville

ICML 2023 The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation Mark Rowland, Yunhao Tang, Clare Lyle, Remi Munos, Marc G Bellemare, Will Dabney

AISTATS 2022 On the Generalization of Representations in Reinforcement Learning Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare

NeurIPSW 2022 A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal Subspaces Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G Bellemare

ICML 2022 Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning Harley E Wiltzer, David Meger, Marc G. Bellemare

ICLRW 2022 Introducing Coordination in Concurrent Reinforcement Learning Adrien Ali Taiga, Aaron Courville, Marc G Bellemare

NeurIPSW 2022 Investigating Multi-Task Pretraining and Generalization in Reinforcement Learning Adrien Ali Taiga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, Marc G Bellemare

NeurIPSW 2022 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

NeurIPSW 2022 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

NeurIPSW 2022 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

NeurIPSW 2022 Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, Aaron Courville

NeurIPSW 2022 Variance Double-Down: The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning Johan Samir Obando Ceron, Marc G Bellemare, Pablo Samuel Castro

ICLR 2021 Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G Bellemare

AAAI 2021 Metrics and Continuity in Reinforcement Learning Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro

ICLR 2021 The Importance of Pessimism in Fixed-Dataset Policy Optimization Jacob Buckman, Carles Gelada, Marc G Bellemare

AAAI 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

AISTATS 2020 A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare

AAAI 2020 Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare

AAAI 2020 Count-Based Exploration with the Successor Representation Marlos C. Machado, Marc G. Bellemare, Michael Bowling

ICLR 2020 Hyperbolic Discounting and Learning over Multiple Horizons William Fedus, Carles Gelada, Yoshua Bengio, Marc G. Bellemare, Hugo Larochelle

ICLR 2020 On Bonus Based Exploration Methods in the Arcade Learning Environment Adrien Ali Taiga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare

ICML 2020 Representations for Stable Off-Policy Reinforcement Learning Dibya Ghosh, Marc G. Bellemare

AAAI 2019 A Comparative Analysis of Expected and Distributional Reinforcement Learning Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro

IJCAI 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

ICML 2019 DeepMDP: Learning Continuous Latent Space Models for Representation Learning Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare

AISTATS 2019 Distributional Reinforcement Learning with Linear Function Approximation Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra

AAAI 2019 Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift Carles Gelada, Marc G. Bellemare

ICML 2019 Statistics and Samples in Distributional Reinforcement Learning Mark Rowland, Robert Dadashi, Saurabh Kumar, Remi Munos, Marc G. Bellemare, Will Dabney

ICML 2019 The Value Function Polytope in Reinforcement Learning Robert Dadashi, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare

AISTATS 2018 An Analysis of Categorical Distributional Reinforcement Learning Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh

FnTML 2018 An Introduction to Deep Reinforcement Learning Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau

AAAI 2018 Distributional Reinforcement Learning with Quantile Regression Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos

JAIR 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

IJCAI 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

ICML 2017 A Distributional Perspective on Reinforcement Learning Marc G. Bellemare, Will Dabney, Rémi Munos

ICML 2017 A Laplacian Framework for Option Discovery in Reinforcement Learning Marlos C. Machado, Marc G. Bellemare, Michael Bowling

ICML 2017 Automated Curriculum Learning for Neural Networks Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu

ICML 2017 Count-Based Exploration with Neural Density Models Georg Ostrovski, Marc G. Bellemare, Aäron Oord, Rémi Munos

AAAI 2016 Increasing the Action Gap: New Operators for Reinforcement Learning Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos

ALT 2016 Q(λ) with Off-Policy Corrections Anna Harutyunyan, Marc G. Bellemare, Tom Stepleton, Rémi Munos

AAAI 2015 Compress and Control Joel Veness, Marc G. Bellemare, Marcus Hutter, Alvin Chua, Guillaume Desjardins

IJCAI 2015 Count-Based Frequency Estimation with Bounded Memory Marc G. Bellemare

IJCAI 2015 Online Learning of K-CNF Boolean Functions Joel Veness, Marcus Hutter, Laurent Orseau, Marc G. Bellemare

IJCAI 2015 The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) Marc G. Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling

JAIR 2013 The Arcade Learning Environment: An Evaluation Platform for General Agents Marc G. Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling

AAAI 2012 Investigating Contingency Awareness Using Atari 2600 Games Marc G. Bellemare, Joel Veness, Michael Bowling

IJCAI 2007 Context-Driven Predictions Marc G. Bellemare, Doina Precup