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2020 – today
- 2024
[j7]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. J. Mach. Learn. Res. 25: 163:1-163:47 (2024)- [c56]Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland:
A Distributional Analogue to the Successor Representation. ICML 2024 - [i57]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 abs/2402.08530 (2024) - [i56]Nate Rahn, Pierluca D'Oro, Marc G. Bellemare:
Controlling Large Language Model Agents with Entropic Activation Steering. CoRR abs/2406.00244 (2024) - 2023
- [c55]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: 1703-1718 - [c54]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 - [c53]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 - [c52]Johan Samir Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro:
The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning. Tiny Papers @ ICLR 2023 - [c51]Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare:
Investigating Multi-task Pretraining and Generalization in Reinforcement Learning. ICLR 2023 - [c50]Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney:
Bootstrapped Representations in Reinforcement Learning. ICML 2023: 18686-18713 - [c49]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: 29210-29231 - [c48]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: 30365-30380 - [c47]Johan S. Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro:
Small batch deep reinforcement learning. NeurIPS 2023 - [c46]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 - [i55]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 abs/2301.04462 (2023) - [i54]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 abs/2304.12567 (2023) - [i53]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 abs/2305.18388 (2023) - [i52]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 abs/2305.19452 (2023) - [i51]Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney:
Bootstrapped Representations in Reinforcement Learning. CoRR abs/2306.10171 (2023) - [i50]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 abs/2309.14597 (2023) - [i49]Johan S. Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro:
Small batch deep reinforcement learning. CoRR abs/2310.03882 (2023) - [i48]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 abs/2311.17894 (2023) - 2022
- [c45]Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare:
On the Generalization of Representations in Reinforcement Learning. AISTATS 2022: 4132-4157 - [c44]Harley E. Wiltzer, David Meger, Marc G. Bellemare:
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning. ICML 2022: 23832-23856 - [c43]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress. NeurIPS 2022 - [c42]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 - [i47]Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare:
On the Generalization of Representations in Reinforcement Learning. CoRR abs/2203.00543 (2022) - [i46]Harley Wiltzer, David Meger, Marc G. Bellemare:
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning. CoRR abs/2205.12184 (2022) - [i45]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Beyond Tabula Rasa: Reincarnating Reinforcement Learning. CoRR abs/2206.01626 (2022) - [i44]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 abs/2207.07570 (2022) - [i43]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 abs/2212.04025 (2022) - 2021
- [c41]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: 7160-7168 - [c40]Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro:
Metrics and Continuity in Reinforcement Learning. AAAI 2021: 8261-8269 - [c39]Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare:
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. ICLR 2021 - [c38]Jacob Buckman, Carles Gelada, Marc G. Bellemare:
The Importance of Pessimism in Fixed-Dataset Policy Optimization. ICLR 2021 - [c37]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: 29304-29320 - [i42]Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare:
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. CoRR abs/2101.05265 (2021) - [i41]Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro:
Metrics and continuity in reinforcement learning. CoRR abs/2102.01514 (2021) - [i40]Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare:
Deep Reinforcement Learning at the Edge of the Statistical Precipice. CoRR abs/2108.13264 (2021) - [i39]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 abs/2109.11052 (2021) - 2020
- [j6]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: 103216 (2020) - [j5]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): 77-82 (2020) - [c36]Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare:
Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction. AAAI 2020: 4328-4336 - [c35]Marlos C. Machado, Marc G. Bellemare, Michael Bowling:
Count-Based Exploration with the Successor Representation. AAAI 2020: 5125-5133 - [c34]Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare:
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. AISTATS 2020: 4357-4366 - [c33]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: 9-16 - [c32]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 - [c31]Dibya Ghosh, Marc G. Bellemare:
Representations for Stable Off-Policy Reinforcement Learning. ICML 2020: 3556-3565 - [i38]William Fedus, Dibya Ghosh, John D. Martin, Marc G. Bellemare, Yoshua Bengio, Hugo Larochelle:
On Catastrophic Interference in Atari 2600 Games. CoRR abs/2002.12499 (2020) - [i37]Ahmed Touati, Adrien Ali Taïga, Marc G. Bellemare:
Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces. CoRR abs/2003.04069 (2020) - [i36]Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare:
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. CoRR abs/2003.12239 (2020) - [i35]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 abs/2006.02243 (2020) - [i34]Dibya Ghosh, Marc G. Bellemare:
Representations for Stable Off-Policy Reinforcement Learning. CoRR abs/2007.05520 (2020) - [i33]Jacob Buckman, Carles Gelada, Marc G. Bellemare:
The Importance of Pessimism in Fixed-Dataset Policy Optimization. CoRR abs/2009.06799 (2020)
2010 – 2019
- 2019
- [c30]Philip Amortila, Marc G. Bellemare, Prakash Panangaden, Doina Precup:
Temporally Extended Metrics for Markov Decision Processes. SafeAI@AAAI 2019 - [c29]Carles Gelada, Marc G. Bellemare:
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift. AAAI 2019: 3647-3655 - [c28]Clare Lyle, Marc G. Bellemare, Pablo Samuel Castro:
A Comparative Analysis of Expected and Distributional Reinforcement Learning. AAAI 2019: 4504-4511 - [c27]Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra:
Distributional reinforcement learning with linear function approximation. AISTATS 2019: 2203-2211 - [c26]Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans:
The Value Function Polytope in Reinforcement Learning. ICML 2019: 1486-1495 - [c25]Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare:
DeepMDP: Learning Continuous Latent Space Models for Representation Learning. ICML 2019: 2170-2179 - [c24]Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney:
Statistics and Samples in Distributional Reinforcement Learning. ICML 2019: 5528-5536 - [c23]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: 3260-3267 - [c22]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: 4360-4371 - [i32]Carles Gelada, Marc G. Bellemare:
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift. CoRR abs/1901.09455 (2019) - [i31]Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare:
A Comparative Analysis of Expected and Distributional Reinforcement Learning. CoRR abs/1901.11084 (2019) - [i30]Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare:
The Value Function Polytope in Reinforcement Learning. CoRR abs/1901.11524 (2019) - [i29]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 abs/1901.11528 (2019) - [i28]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 abs/1901.11530 (2019) - [i27]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 abs/1902.00506 (2019) - [i26]Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro, Subhodeep Moitra:
Distributional reinforcement learning with linear function approximation. CoRR abs/1902.03149 (2019) - [i25]William Fedus, Carles Gelada, Yoshua Bengio, Marc G. Bellemare, Hugo Larochelle:
Hyperbolic Discounting and Learning over Multiple Horizons. CoRR abs/1902.06865 (2019) - [i24]Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney:
Statistics and Samples in Distributional Reinforcement Learning. CoRR abs/1902.08102 (2019) - [i23]Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare:
DeepMDP: Learning Continuous Latent Space Models for Representation Learning. CoRR abs/1906.02736 (2019) - [i22]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 abs/1908.02388 (2019) - [i21]Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare:
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction. CoRR abs/1911.12511 (2019) - 2018
- [j4]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): 219-354 (2018) - [j3]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: 523-562 (2018) - [c21]Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos:
Distributional Reinforcement Learning With Quantile Regression. AAAI 2018: 2892-2901 - [c20]Mark Rowland, Marc G. Bellemare, Will Dabney, Rémi Munos, Yee Whye Teh:
An Analysis of Categorical Distributional Reinforcement Learning. AISTATS 2018: 29-37 - [c19]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 - [c18]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: 5573-5577 - [i20]Marlos C. Machado, Marc G. Bellemare, Michael Bowling:
Count-Based Exploration with the Successor Representation. CoRR abs/1807.11622 (2018) - [i19]Adrien Ali Taïga, Aaron C. Courville, Marc G. Bellemare:
Approximate Exploration through State Abstraction. CoRR abs/1808.09819 (2018) - [i18]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 abs/1811.07004 (2018) - [i17]Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau:
An Introduction to Deep Reinforcement Learning. CoRR abs/1811.12560 (2018) - [i16]Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, Marc G. Bellemare:
Dopamine: A Research Framework for Deep Reinforcement Learning. CoRR abs/1812.06110 (2018) - [i15]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 abs/1812.07069 (2018) - 2017
- [c17]Marc G. Bellemare, Will Dabney, Rémi Munos:
A Distributional Perspective on Reinforcement Learning. ICML 2017: 449-458 - [c16]Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu:
Automated Curriculum Learning for Neural Networks. ICML 2017: 1311-1320 - [c15]Marlos C. Machado, Marc G. Bellemare, Michael H. Bowling:
A Laplacian Framework for Option Discovery in Reinforcement Learning. ICML 2017: 2295-2304 - [c14]Georg Ostrovski, Marc G. Bellemare, Aäron van den Oord, Rémi Munos:
Count-Based Exploration with Neural Density Models. ICML 2017: 2721-2730 - [i14]Marlos C. Machado, Marc G. Bellemare, Michael H. Bowling:
A Laplacian Framework for Option Discovery in Reinforcement Learning. CoRR abs/1703.00956 (2017) - [i13]Georg Ostrovski, Marc G. Bellemare, Aäron van den Oord, Rémi Munos:
Count-Based Exploration with Neural Density Models. CoRR abs/1703.01310 (2017) - [i12]Alex Graves, Marc G. Bellemare, Jacob Menick, Rémi Munos, Koray Kavukcuoglu:
Automated Curriculum Learning for Neural Networks. CoRR abs/1704.03003 (2017) - [i11]Audrunas Gruslys, Mohammad Gheshlaghi Azar, Marc G. Bellemare, Rémi Munos:
The Reactor: A Sample-Efficient Actor-Critic Architecture. CoRR abs/1704.04651 (2017) - [i10]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 abs/1705.10743 (2017) - [i9]Marc G. Bellemare, Will Dabney, Rémi Munos:
A Distributional Perspective on Reinforcement Learning. CoRR abs/1707.06887 (2017) - [i8]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 abs/1709.06009 (2017) - [i7]Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos:
Distributional Reinforcement Learning with Quantile Regression. CoRR abs/1710.10044 (2017) - 2016
- [c13]Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos:
Increasing the Action Gap: New Operators for Reinforcement Learning. AAAI 2016: 1476-1483 - [c12]Anna Harutyunyan, Marc G. Bellemare, Tom Stepleton, Rémi Munos:
Q(λ) with Off-Policy Corrections. ALT 2016: 305-320 - [c11]Rémi Munos, Tom Stepleton, Anna Harutyunyan, Marc G. Bellemare:
Safe and Efficient Off-Policy Reinforcement Learning. NIPS 2016: 1046-1054 - [c10]Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos:
Unifying Count-Based Exploration and Intrinsic Motivation. NIPS 2016: 1471-1479 - [i6]Anna Harutyunyan, Marc G. Bellemare, Tom Stepleton, Rémi Munos:
Q($λ$) with Off-Policy Corrections. CoRR abs/1602.04951 (2016) - [i5]Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos:
Unifying Count-Based Exploration and Intrinsic Motivation. CoRR abs/1606.01868 (2016) - [i4]Rémi Munos, Tom Stepleton, Anna Harutyunyan, Marc G. Bellemare:
Safe and Efficient Off-Policy Reinforcement Learning. CoRR abs/1606.02647 (2016) - 2015
- [j2]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:
Human-level control through deep reinforcement learning. Nat. 518(7540): 529-533 (2015) - [c9]Joel Veness, Marc G. Bellemare, Marcus Hutter, Alvin Chua, Guillaume Desjardins:
Compress and Control. AAAI 2015: 3016-3023 - [c8]Marc G. Bellemare:
Count-Based Frequency Estimation with Bounded Memory. IJCAI 2015: 3337-3344 - [c7]Joel Veness, Marcus Hutter, Laurent Orseau, Marc G. Bellemare:
Online Learning of k-CNF Boolean Functions. IJCAI 2015: 3865-3873 - [c6]Marc G. Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling:
The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract). IJCAI 2015: 4148-4152 - [e1]Michael Bowling, Marc G. Bellemare, Erik Talvitie, Joel Veness, Marlos C. Machado:
Learning for General Competency in Video Games, Papers from the 2015 AAAI Workshop, Austin, Texas, USA, January 26, 2015. AAAI Technical Report WS-15-10, AAAI Press 2015, ISBN 978-1-57735-721-6 [contents] - [i3]Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos:
Increasing the Action Gap: New Operators for Reinforcement Learning. CoRR abs/1512.04860 (2015) - 2014
- [c5]Marc G. Bellemare, Joel Veness, Erik Talvitie:
Skip Context Tree Switching. ICML 2014: 1458-1466 - [i2]Joel Veness, Marc G. Bellemare, Marcus Hutter, Alvin Chua, Guillaume Desjardins:
Compress and Control. CoRR abs/1411.5326 (2014) - 2013
- [j1]Marc G. Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling:
The Arcade Learning Environment: An Evaluation Platform for General Agents. J. Artif. Intell. Res. 47: 253-279 (2013) - [c4]Marc G. Bellemare, Joel Veness, Michael Bowling:
Bayesian Learning of Recursively Factored Environments. ICML (3) 2013: 1211-1219 - 2012
- [c3]Marc G. Bellemare, Joel Veness, Michael Bowling:
Investigating Contingency Awareness Using Atari 2600 Games. AAAI 2012: 864-871 - [c2]Marc G. Bellemare, Joel Veness, Michael Bowling:
Sketch-Based Linear Value Function Approximation. NIPS 2012: 2222-2230 - [i1]Marc G. Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling:
The Arcade Learning Environment: An Evaluation Platform for General Agents. CoRR abs/1207.4708 (2012)
2000 – 2009
- 2007
- [c1]Marc G. Bellemare, Doina Precup:
Context-Driven Predictions. IJCAI 2007: 250-255
Coauthor Index
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