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

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

Sketch-Based Linear Value Function Approximation. - Marc G. Bellemare; Joel Veness; Michael Bowling (NIPS, 2012)

Bayesian Learning of Recursively Factored Environments. - Marc G. Bellemare; Joel Veness; Michael Bowling (ICML (3), 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)

Skip Context Tree Switching. - Marc G. Bellemare; Joel Veness; Erik Talvitie (ICML, 2014)

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

Count-Based Frequency Estimation with Bounded Memory. - 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 (IJCAI, 2015)

Online Learning of k-CNF Boolean Functions. - Joel Veness; Marcus Hutter; Laurent Orseau; Marc G. Bellemare (IJCAI, 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; et al. (Nat., 2015)

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

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

Unifying Count-Based Exploration and Intrinsic Motivation. - Marc G. Bellemare; Sriram Srinivasan 005; Georg Ostrovski; Tom Schaul; David Saxton; Rémi Munos (NIPS, 2016)

Safe and Efficient Off-Policy Reinforcement Learning. - Rémi Munos; Tom Stepleton; Anna Harutyunyan; Marc G. Bellemare (NIPS, 2016)

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

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

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

Count-Based Exploration with Neural Density Models. - Georg Ostrovski; Marc G. Bellemare; Aäron van den Oord; Rémi Munos (ICML, 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)

The Reactor: A Sample-Efficient Actor-Critic Architecture. - Audrunas Gruslys; Mohammad Gheshlaghi Azar; Marc G. Bellemare; Rémi Munos (CoRR, 2017)

Distributional Reinforcement Learning With Quantile Regression. - Will Dabney; Mark Rowland 001; Marc G. Bellemare; Rémi Munos (AAAI, 2018)

An Analysis of Categorical Distributional Reinforcement Learning. - Mark Rowland 001; Marc G. Bellemare; Will Dabney; Rémi Munos; Yee Whye Teh (AISTATS, 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 (ICLR (Poster), 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 (IJCAI, 2018)

Approximate Exploration through State Abstraction. - Adrien Ali Taïga; Aaron C. Courville; 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 001; Pierre-Luc Bacon; Jean Harb; Shibl Mourad; Marc G. Bellemare; Doina Precup (CoRR, 2018)

Dopamine: A Research Framework for Deep Reinforcement Learning. - Pablo Samuel Castro; Subhodeep Moitra; Carles Gelada; Saurabh Kumar 004; Marc G. Bellemare (CoRR, 2018)

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents. - Felipe Petroski Such; Vashisht Madhavan; Rosanne Liu; Rui Wang 052; Pablo Samuel Castro; Yulun Li; Ludwig Schubert; Marc G. Bellemare; Jeff Clune; Joel Lehman (CoRR, 2018)

An Introduction to Deep Reinforcement Learning. - Vincent François-Lavet; Peter Henderson 002; Riashat Islam; Marc G. Bellemare; Joelle Pineau (Found. Trends Mach. Learn., 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)

Temporally Extended Metrics for Markov Decision Processes. - Philip Amortila; Marc G. Bellemare; Prakash Panangaden; Doina Precup (SafeAI@AAAI, 2019)

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

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

Distributional reinforcement learning with linear function approximation. - Marc G. Bellemare; Nicolas Le Roux; Pablo Samuel Castro; Subhodeep Moitra (AISTATS, 2019)

The Value Function Polytope in Reinforcement Learning. - Robert Dadashi; Marc G. Bellemare; Adrien Ali Taïga; Nicolas Le Roux; Dale Schuurmans (ICML, 2019)

DeepMDP: Learning Continuous Latent Space Models for Representation Learning. - Carles Gelada; Saurabh Kumar 004; Jacob Buckman; Ofir Nachum; Marc G. Bellemare (ICML, 2019)

Statistics and Samples in Distributional Reinforcement Learning. - Mark Rowland 001; Robert Dadashi; Saurabh Kumar 004; Rémi Munos; Marc G. Bellemare; Will Dabney (ICML, 2019)

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents. - Felipe Petroski Such; Vashisht Madhavan; Rosanne Liu; Rui Wang 052; Pablo Samuel Castro; Yulun Li; Jiale Zhi; Ludwig Schubert; Marc G. Bellemare; Jeff Clune; Joel Lehman (IJCAI, 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 (NeurIPS, 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)

Hyperbolic Discounting and Learning over Multiple Horizons. - William Fedus; Carles Gelada; Yoshua Bengio; Marc G. Bellemare; Hugo Larochelle (CoRR, 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)

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 (AAAI, 2020)

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

Representations for Stable Off-Policy Reinforcement Learning. - Dibya Ghosh; Marc G. Bellemare (ICML, 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 001; Iain Dunning; Shibl Mourad; et al. (Artif. Intell., 2020)

On Catastrophic Interference in Atari 2600 Games. - William Fedus; Dibya Ghosh; John D. Martin; Marc G. Bellemare; Yoshua Bengio; Hugo Larochelle (CoRR, 2020)

Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces. - Ahmed Touati; Adrien Ali Taïga; Marc G. Bellemare (CoRR, 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)

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. - Will Dabney; André Barreto 001; Mark Rowland 001; Robert Dadashi; John Quan; Marc G. Bellemare; David Silver (AAAI, 2021)

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

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

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

Deep Reinforcement Learning at the Edge of the Statistical Precipice. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (NeurIPS, 2021)

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

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

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (NeurIPS, 2022)

The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. - Yunhao Tang; Rémi Munos; Mark Rowland 001; Bernardo Ávila Pires; Will Dabney; Marc G. Bellemare (NeurIPS, 2022)

Beyond Tabula Rasa: Reincarnating Reinforcement Learning. - Rishabh Agarwal; Max Schwarzer; Pablo Samuel Castro; Aaron C. Courville; Marc G. Bellemare (CoRR, 2022)

A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. - Charline Le Lan; Joshua Greaves; Jesse Farebrother; Mark Rowland 001; Fabian Pedregosa; Rishabh Agarwal; Marc G. Bellemare (AISTATS, 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 (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)

The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning. - Johan S. Obando-Ceron; Marc G. Bellemare; Pablo Samuel Castro (Tiny Papers @ ICLR, 2023)

Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. - Adrien Ali Taïga; Rishabh Agarwal; Jesse Farebrother; Aaron C. Courville; Marc G. Bellemare (ICLR, 2023)

Bootstrapped Representations in Reinforcement Learning. - Charline Le Lan; Stephen Tu; Mark Rowland 001; Anna Harutyunyan; Rishabh Agarwal; Marc G. Bellemare; Will Dabney (ICML, 2023)

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. - Mark Rowland 001; Yunhao Tang; Clare Lyle; Rémi Munos; Marc G. Bellemare; Will Dabney (ICML, 2023)

Bigger, Better, Faster: Human-level Atari with human-level efficiency. - Max Schwarzer; Johan S. Obando-Ceron; Aaron C. Courville; Marc G. Bellemare; Rishabh Agarwal; Pablo Samuel Castro (ICML, 2023)

Small batch deep reinforcement learning. - Johan S. Obando-Ceron; Marc G. Bellemare; Pablo Samuel Castro (NeurIPS, 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 (NeurIPS, 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)

A Distributional Analogue to the Successor Representation. - Harley Wiltzer; Jesse Farebrother; Arthur Gretton; Yunhao Tang; André Barreto 001; Will Dabney; Marc G. Bellemare; Mark Rowland 001 (ICML, 2024)

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

Controlling Large Language Model Agents with Entropic Activation Steering. - Nate Rahn; Pierluca D'Oro; Marc G. Bellemare (CoRR, 2024)

An Analysis of Quantile Temporal-Difference Learning. - Mark Rowland 001; Rémi Munos; Mohammad Gheshlaghi Azar; Yunhao Tang; Georg Ostrovski; Anna Harutyunyan; Karl Tuyls; Marc G. Bellemare; Will Dabney (J. Mach. Learn. Res., 2024)

Tapered Off-Policy REINFORCE: Stable and efficient reinforcement learning for LLMs. - Nicolas Le Roux; Marc G. Bellemare; Jonathan Lebensold; Arnaud Bergeron; Joshua Greaves; Alexandre Fréchette; Carolyne Pelletier; Eric Thibodeau-Laufer; Sándor Tóth; Sam Work (CoRR, 2025)