Marc G. Bellemare

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All published works

Action	Title	Year	Authors
+ PDF Chat	Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning	2024	Harley Wiltzer Marc G. Bellemare David Meger Patrick Shafto Yash Jhaveri
+ PDF Chat	Controlling Large Language Model Agents with Entropic Activation Steering	2024	Nate Rahn Pierluca D’Oro Marc G. Bellemare
+ PDF Chat	A Distributional Analogue to the Successor Representation	2024	Harley Wiltzer Jesse Farebrother Arthur Gretton Yunhao Tang André Barreto Will Dabney Marc G. Bellemare Mark Rowland
+	An Analysis of Quantile Temporal-Difference Learning	2023	Mark Rowland Rémi Munos Mohammad Gheshlaghi Azar Yunhao Tang Georg Ostrovski Anna Harutyunyan Karl Tuyls Marc G. Bellemare Will Dabney
+	Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks	2023	Jesse Farebrother Joshua Greaves Rishabh Agarwal Charline Le Lan Ross Goroshin Pablo Samuel Castro Marc G. Bellemare
+	The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation	2023	Mark Rowland Yunhao Tang Clare Lyle Rémi Munos Marc G. Bellemare Will Dabney
+	Bootstrapped Representations in Reinforcement Learning	2023	Charline Le Lan Stephen Tu Mark Rowland Anna Harutyunyan Rishabh Agarwal Marc G. Bellemare Will Dabney
+	Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control	2023	Nate Rahn Pierluca D’Oro Harley Wiltzer Pierre‐Luc Bacon Marc G. Bellemare
+	Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy	2023	Max Schwarzer Jesse Farebrother Joshua Greaves Ekin D. Cubuk Rishabh Agarwal Aaron Courville Marc G. Bellemare Sergei V. Kalinin Igor Mordatch Pablo Samuel Castro
+	Small batch deep reinforcement learning	2023	Johan S. Obando-Ceron Marc G. Bellemare Pablo Samuel Castro
+	On the Generalization of Representations in Reinforcement Learning	2022	Charline Le Lan Stephen Tu Adam M. Oberman Rishabh Agarwal Marc G. Bellemare
+	Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning	2022	Harley Wiltzer David Meger Marc G. Bellemare
+	Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress	2022	Rishabh Agarwal Max Schwarzer Pablo Samuel Castro Aaron Courville Marc G. Bellemare
+	A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces	2022	Charline Le Lan Joshua Greaves Jesse Farebrother Mark Rowland Fabián Pedregosa Rishabh Agarwal Marc G. Bellemare
+	The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning	2022	Yunhao Tang Mark Rowland Rémi Munos Bernardo Ávila Pires Will Dabney Marc G. Bellemare
+	Deep Reinforcement Learning at the Edge of the Statistical Precipice	2021	Rishabh Agarwal Max Schwarzer Pablo Samuel Castro Aaron Courville Marc G. Bellemare
+	Deep Reinforcement Learning at the Edge of the Statistical Precipice	2021	Rishabh Agarwal Max Schwarzer Pablo Samuel Castro Aaron Courville Marc G. Bellemare
+ PDF Chat	Metrics and Continuity in Reinforcement Learning	2021	Charline Le Lan Marc G. Bellemare Pablo Samuel Castro
+ PDF Chat	The Value-Improvement Path: Towards Better Representations for Reinforcement Learning	2021	Will Dabney André Barreto Mark Rowland Robert Dadashi John Quan Marc G. Bellemare David Silver
+	Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning	2021	Rishabh Agarwal Marlos C. Machado Pablo Samuel Castro Marc G. Bellemare
+	Metrics and continuity in reinforcement learning	2021	Charline Le Lan Marc G. Bellemare Pablo Samuel Castro
+	Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning	2021	Rishabh Agarwal Marlos C. Machado Pablo Samuel Castro Marc G. Bellemare
+	On Bonus-Based Exploration Methods in the Arcade Learning Environment	2021	Adrien Ali Taïga William Fedus Marlos C. Machado Aaron Courville Marc G. Bellemare
+	Deep Reinforcement Learning at the Edge of the Statistical Precipice	2021	Rishabh Agarwal Max Schwarzer Pablo Samuel Castro Aaron Courville Marc G. Bellemare
+	Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning	2021	Rishabh Agarwal Marlos C. Machado Pablo Samuel Castro Marc G. Bellemare
+	Metrics and continuity in reinforcement learning	2021	Charline Le Lan Marc G. Bellemare Pablo Samuel Castro
+	Representations for Stable Off-Policy Reinforcement Learning	2020	Dibya Ghosh Marc G. Bellemare
+	The Value-Improvement Path: Towards Better Representations for Reinforcement Learning.	2020	Will Dabney André Barreto Mark Rowland Robert Dadashi John Quan Marc G. Bellemare David Silver
+	On Bonus Based Exploration Methods In The Arcade Learning Environment	2020	Adrien Ali Taïga William Fedus Marlos C. Machado Aaron Courville Marc G. Bellemare
+ PDF Chat	Count-Based Exploration with the Successor Representation	2020	Marlos C. Machado Marc G. Bellemare Michael Bowling
+ PDF Chat	Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction	2020	Vishal Jain William Fedus Hugo Larochelle Doina Precup Marc G. Bellemare
+	A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms	2020	Philip Amortila Doina Precup Prakash Panangaden Marc G. Bellemare
+	Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces	2020	Abdelaziz Touati Adrien Ali Taïga Marc G. Bellemare
+	On Catastrophic Interference in Atari 2600 Games	2020	William Fedus Dibya Ghosh John D. Martin Marc G. Bellemare Yoshua Bengio Hugo Larochelle
+	Representations for Stable Off-Policy Reinforcement Learning	2020	Dibya Ghosh Marc G. Bellemare
+	The Importance of Pessimism in Fixed-Dataset Policy Optimization	2020	Jacob Buckman Carles Gelada Marc G. Bellemare
+	A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms	2020	Philip Amortila Doina Precup Prakash Panangaden Marc G. Bellemare
+	On Catastrophic Interference in Atari 2600 Games	2020	William Fedus Dibya Ghosh John D. Martin Marc G. Bellemare Yoshua Bengio Hugo Larochelle
+	Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces	2020	Abdelaziz Touati Adrien Ali Taïga Marc G. Bellemare
+	The Value-Improvement Path: Towards Better Representations for Reinforcement Learning	2020	Will Dabney André Sales Barreto Mark Rowland Robert Dadashi John Quan Marc G. Bellemare David Silver
+	Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction	2019	Vishal Jain William Fedus Hugo Larochelle Doina Precup Marc G. Bellemare
+	The Hanabi challenge: A new frontier for AI research	2019	Nolan Bard Jakob Foerster Sarath Chandar Neil Burch Marc Lanctot Hai-Jing Song Emilio Parisotto Vincent Dumoulin Subhodeep Moitra Edward Hughes
+	Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment	2019	Adrien Ali Taïga William Fedus Marlos C. Machado Aaron Courville Marc G. Bellemare
+	An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents	2019	Felipe Petroski Such Vashisht Madhavan Rosanne Liu Rui Wang Pablo Samuel Castro Yulun Li Jiale Zhi Ludwig Schubert Marc G. Bellemare Jeff Clune
+ PDF Chat	Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift	2019	Carles Gelada Marc G. Bellemare
+ PDF Chat	A Comparative Analysis of Expected and Distributional Reinforcement Learning	2019	Clare Lyle Marc G. Bellemare Pablo Samuel Castro
+	DeepMDP: Learning Continuous Latent Space Models for Representation Learning	2019	Carles Gelada Saurabh Kumar Jacob Buckman Ofir Nachum Marc G. Bellemare
+	The Value Function Polytope in Reinforcement Learning	2019	Robert Dadashi Adrien Ali Taïga Nicolas Le Roux Dale Schuurmans Marc G. Bellemare
+	Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue.	2019	Kory W. Mathewson Pablo Samuel Castro Colin Cherry George Foster Marc G. Bellemare
+	The Value Function Polytope in Reinforcement Learning	2019	Robert Dadashi Adrien Ali Taïga Nicolas Le Roux Dale Schuurmans Marc G. Bellemare
+	A Comparative Analysis of Expected and Distributional Reinforcement Learning	2019	Clare Lyle Pablo Samuel Castro Marc G. Bellemare
+	Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift	2019	Carles Gelada Marc G. Bellemare
+	A Geometric Perspective on Optimal Representations for Reinforcement Learning	2019	Marc G. Bellemare Will Dabney Robert Dadashi Adrien Ali Taïga Pablo Samuel Castro Nicolas Le Roux Dale Schuurmans Tor Lattimore Clare Lyle
+	Hyperbolic Discounting and Learning over Multiple Horizons	2019	William Fedus Carles Gelada Yoshua Bengio Marc G. Bellemare Hugo Larochelle
+	Statistics and Samples in Distributional Reinforcement Learning	2019	Mark Rowland Robert Dadashi Saurabh Kumar Rémi Munos Marc G. Bellemare Will Dabney
+	Distributional reinforcement learning with linear function approximation	2019	Marc G. Bellemare Nicolas Le Roux Pablo Samuel Castro Subhodeep Moitra
+	Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction	2019	Vishal Jain William Fedus Hugo Larochelle Doina Precup Marc G. Bellemare
+	Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment	2019	Adrien Ali Taïga William Fedus Marlos C. Machado Aaron Courville Marc G. Bellemare
+	DeepMDP: Learning Continuous Latent Space Models for Representation Learning	2019	Carles Gelada Saurabh Kumar Jacob Buckman Ofir Nachum Marc G. Bellemare
+	The Value Function Polytope in Reinforcement Learning	2019	Robert Dadashi Adrien Ali Taïga Nicolas Le Roux Dale Schuurmans Marc G. Bellemare
+	Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue	2019	Kory W. Mathewson Pablo Samuel Castro Colin Cherry George Foster Marc G. Bellemare
+	A Comparative Analysis of Expected and Distributional Reinforcement Learning	2019	Clare Lyle Pablo Samuel Castro Marc G. Bellemare
+	Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift	2019	Carles Gelada Marc G. Bellemare
+	An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents	2018	Felipe Petroski Such Vashisht Madhavan Rosanne Liu Rui Wang Pablo Samuel Castro Yulun Li Jiale Zhi Ludwig Schubert Marc G. Bellemare Jeff Clune
+	The Barbados 2018 List of Open Issues in Continual Learning.	2018	Tom Schaul Hado van Hasselt Joseph Modayil Martha White Adam White Pierre‐Luc Bacon Jean Harb Shibl Mourad Marc G. Bellemare Doina Precup
+	Approximate Exploration through State Abstraction.	2018	Adrien Ali Taïga Aaron Courville Marc G. Bellemare
+	Count-Based Exploration with the Successor Representation	2018	Marlos C. Machado Marc G. Bellemare Michael Bowling
+ PDF Chat	Distributional Reinforcement Learning With Quantile Regression	2018	Will Dabney Mark Rowland Marc G. Bellemare Rémi Munos
+ PDF Chat	Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents	2018	Marlos C. Machado Marc G. Bellemare Erik Talvitie Joel Veness Matthew Hausknecht Michael Bowling
+	An Analysis of Categorical Distributional Reinforcement Learning	2018	Mark Rowland Marc G. Bellemare Will Dabney Rémi Munos Yee Whye Teh
+	Dopamine: A Research Framework for Deep Reinforcement Learning	2018	Pablo Samuel Castro Subhodeep Moitra Carles Gelada Saurabh Kumar Marc G. Bellemare
+ PDF Chat	An Introduction to Deep Reinforcement Learning	2018	Vincent François-Lavet Peter Henderson Riashat Islam Marc G. Bellemare Joëlle Pineau
+ PDF Chat	An Introduction to Deep Reinforcement Learning	2018	Vincent François-Lavet Peter Henderson Riashat Islam Marc G. Bellemare Joëlle Pineau
+	An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents	2018	Felipe Petroski Such Vashisht Madhavan Rosanne Liu Rui Wang Pablo Samuel Castro Yulun Li Jiale Zhi Ludwig Schubert Marc G. Bellemare Jeff Clune
+	Approximate Exploration through State Abstraction	2018	Adrien Ali Taïga Aaron Courville Marc G. Bellemare
+	Count-Based Exploration with the Successor Representation	2018	Marlos C. Machado Marc G. Bellemare Michael Bowling
+	Distributional Reinforcement Learning With Quantile Regression	2017	Will Dabney Mark Rowland Marc G. Bellemare Rémi Munos
+	Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents	2017	Marlos C. Machado Marc G. Bellemare Erik Talvitie Joel Veness Matthew Hausknecht Michael Bowling
+	A Distributional Perspective on Reinforcement Learning	2017	Marc G. Bellemare Will Dabney Rémi Munos
+	The Reactor: A Sample-Efficient Actor-Critic Architecture	2017	Audrūnas Gruslys Mohammad Gheshlaghi Azar Marc G. Bellemare Rémi Munos
+	The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning	2017	Audrūnas Gruslys Will Dabney Mohammad Gheshlaghi Azar Bilal Piot Marc G. Bellemare Rémi Munos
+	Automated Curriculum Learning for Neural Networks	2017	Alex Graves Marc G. Bellemare Jacob Menick Rémi Munos Koray Kavukcuoglu
+	A Laplacian Framework for Option Discovery in Reinforcement Learning	2017	Marlos C. Machado Marc G. Bellemare Michael Bowling
+	Count-Based Exploration with Neural Density Models	2017	Georg Ostrovski Marc G. Bellemare Aäron van den Oord Rémi Munos
+	The Cramer Distance as a Solution to Biased Wasserstein Gradients	2017	Marc G. Bellemare Ivo Danihelka Will Dabney Shakir Mohamed Balaji Lakshminarayanan Stephan Hoyer Rémi Munos
+	A Laplacian Framework for Option Discovery in Reinforcement Learning	2017	Marlos C. Machado Marc G. Bellemare Michael Bowling
+	Automated Curriculum Learning for Neural Networks	2017	Alex Graves Marc G. Bellemare Jacob Menick Rémi Munos Koray Kavukcuoglu
+	A Distributional Perspective on Reinforcement Learning	2017	Marc G. Bellemare Will Dabney Rémi Munos
+	Distributional Reinforcement Learning with Quantile Regression	2017	Will Dabney Mark Rowland Marc G. Bellemare Rémi Munos
+	Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents	2017	Marlos C. Machado Marc G. Bellemare Erik Talvitie Joel Veness Matthew Hausknecht Michael Bowling
+	Safe and efficient off-policy reinforcement learning	2016	Rémi Munos Thomas Stepleton Anna Harutyunyan Marc G. Bellemare
+ PDF Chat	Increasing the Action Gap: New Operators for Reinforcement Learning	2016	Marc G. Bellemare Georg Ostrovski Arthur Guez Philip S. Thomas Rémi Munos
+	Q($\lambda$) with Off-Policy Corrections	2016	Anna Harutyunyan Marc G. Bellemare Tom Stepleton Rémi Munos
+	Unifying Count-Based Exploration and Intrinsic Motivation	2016	Marc G. Bellemare Sriram Srinivasan Georg Ostrovski Tom Schaul David Saxton Rémi Munos
+ PDF Chat	Q($$\lambda $$) with Off-Policy Corrections	2016	Anna Harutyunyan Marc G. Bellemare Tom Stepleton Rémi Munos
+	Safe and Efficient Off-Policy Reinforcement Learning	2016	Rémi Munos Tom Stepleton Anna Harutyunyan Marc G. Bellemare
+	Q($λ$) with Off-Policy Corrections	2016	Anna Harutyunyan Marc G. Bellemare Tom Stepleton Rémi Munos
+	Increasing the Action Gap: New Operators for Reinforcement Learning	2015	Marc G. Bellemare Georg Ostrovski Arthur Guez Philip S. Thomas Rémi Munos
+	Increasing the Action Gap: New Operators for Reinforcement Learning	2015	Marc G. Bellemare Georg Ostrovski Arthur Guez Philip S. Thomas Rémi Munos
+	Compress and Control	2014	Joel Veness Marc G. Bellemare Marcus Hütter Alvin J. K. Chua Guillaume Desjardins
+	Online Learning of k-CNF Boolean Functions	2014	Joel Veness Marcus Hütter Laurent Orseau Marc G. Bellemare
+	Compress and Control	2014	Joel Veness Marc G. Bellemare Marcus Hütter Alvin J. K. Chua Guillaume Desjardins
+ PDF Chat	The Arcade Learning Environment: An Evaluation Platform for General Agents	2013	Marc G. Bellemare Yavar Naddaf Joel Veness Michael Bowling

Common Coauthors

Coauthor	Papers Together
Rémi Munos	25
Pablo Samuel Castro	24
Will Dabney	18
Marlos C. Machado	15
Adrien Ali Taïga	12
Rishabh Agarwal	11
Aaron Courville	11
Michael Bowling	10
William Fedus	10
Carles Gelada	8
Robert Dadashi	8
Hugo Larochelle	7
Joel Veness	7
Mark Rowland	7
Charline Le Lan	7
Anna Harutyunyan	7
Mark Rowland	7
Doina Precup	6
Georg Ostrovski	6
Clare Lyle	5
Max Schwarzer	5
Nicolas Le Roux	5
Dale Schuurmans	4
Dibya Ghosh	4
Jesse Farebrother	4
Tom Stepleton	4
Yunhao Tang	4
Harley Wiltzer	4
Jacob Buckman	3
Rui Wang	3
John Quan	3
Matthew Hausknecht	3
Rosanne Liu	3
Felipe Petroski Such	3
Jeff Clune	3
Mohammad Gheshlaghi Azar	3
Arthur Guez	3
Vishal Jain	3
Joel Lehman	3
Saurabh Kumar	3
Yulun Li	3
Erik Talvitie	3
Joshua Greaves	3
Jiale Zhi	3
Vashisht Madhavan	3
Subhodeep Moitra	3
Ludwig Schubert	3
Marcus Hütter	3
Yoshua Bengio	3
David Silver	3

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	The Arcade Learning Environment: An Evaluation Platform for General Agents	2013	Marc G. Bellemare Yavar Naddaf Joel Veness Michael Bowling	33
+	Reinforcement Learning with Unsupervised Auxiliary Tasks	2016	Max Jaderberg Volodymyr Mnih Wojciech Marian Czarnecki Tom Schaul Joel Z. Leibo David Silver Koray Kavukcuoglu	14
+	Asynchronous Methods for Deep Reinforcement Learning	2016	Volodymyr Mnih Adrià Puigdomènech Badia Mehdi Mirza Alex Graves Tim Harley Timothy Lillicrap David Silver Koray Kavukcuoglu	13
+	Prioritized Experience Replay	2015	Tom Schaul John Quan Ioannis Antonoglou David Silver	11
+	Deep reinforcement learning with double Q-Learning	2016	Hado van Hasselt Arthur Guez David Silver	10
+	Dopamine: A Research Framework for Deep Reinforcement Learning	2018	Pablo Samuel Castro Subhodeep Moitra Carles Gelada Saurabh Kumar Marc G. Bellemare	10
+ PDF Chat	Rainbow: Combining Improvements in Deep Reinforcement Learning	2018	Matteo Hessel Joseph Modayil Hado van Hasselt Tom Schaul Georg Ostrovski Will Dabney Dan Horgan Bilal Piot Mohammad Gheshlaghi Azar David Silver	9
+ PDF Chat	Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents	2018	Marlos C. Machado Marc G. Bellemare Erik Talvitie Joel Veness Matthew Hausknecht Michael Bowling	8
+	Pixel Recurrent Neural Networks	2016	Aäron van den Oord Nal Kalchbrenner Koray Kavukcuoglu	7
+	The Cramer Distance as a Solution to Biased Wasserstein Gradients	2017	Marc G. Bellemare Ivo Danihelka Will Dabney Shakir Mohamed Balaji Lakshminarayanan Stephan Hoyer Rémi Munos	7
+	IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures	2018	Lasse Espeholt Hubert Soyer Rémi Munos Karen Simonyan Volodymir Mnih Tom Ward Yotam Doron Vlad Firoiu Tim Harley Iain Dunning	7
+	Rainbow: Combining Improvements in Deep Reinforcement Learning	2017	Matteo Hessel Joseph Modayil Hado van Hasselt Tom Schaul Georg Ostrovski Will Dabney Daniel Horgan Bilal Piot Mohammad Gheshlaghi Azar David Silver	7
+	Distributional Reinforcement Learning With Quantile Regression	2017	Will Dabney Mark Rowland Marc G. Bellemare Rémi Munos	7
+	A Distributional Perspective on Reinforcement Learning	2017	Marc G. Bellemare Will Dabney Rémi Munos	6
+	A Distributional Perspective on Reinforcement Learning	2017	Marc G. Bellemare Will Dabney Rémi Munos	6
+	Prioritized Experience Replay	2015	Tom Schaul John Quan Ioannis Antonoglou David Silver	6
+ PDF Chat	Increasing the Action Gap: New Operators for Reinforcement Learning	2016	Marc G. Bellemare Georg Ostrovski Arthur Guez Philip S. Thomas Rémi Munos	6
+	Proximal Policy Optimization Algorithms	2017	John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov	5
+	Evolution Strategies as a Scalable Alternative to Reinforcement Learning	2017	Tim Salimans Jonathan Ho Xi Chen Ilya Sutskever	5
+	Optimal Transport: Old and New	2013	Cédric Villani	5
+	Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models	2015	Bradly C. Stadie Sergey Levine Pieter Abbeel	5
+	Exploration by Random Network Distillation	2018	Yuri Burda Harrison Edwards Amos Storkey Oleg Klimov	5
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	5
+	Action-Conditional Video Prediction using Deep Networks in Atari Games	2015	Junhyuk Oh Xiaoxiao Guo Honglak Lee Richard L. Lewis Satinder Singh	5
+	Massively Parallel Methods for Deep Reinforcement Learning	2015	Arun Sukumaran Nair P. Srinivasan Sam Blackwell Cagdas Alcicek Rory Fearon Alessandro De Maria Vedavyas Panneershelvam Mustafa Suleyman Charles Beattie Stig Petersen	5
+ PDF Chat	Deep Reinforcement Learning with Double Q-Learning	2016	Hado van Hasselt Arthur Guez David Silver	4
+	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	2017	Chelsea Finn Pieter Abbeel Sergey Levine	4
+	Implicit Quantile Networks for Distributional Reinforcement Learning	2018	Will Dabney Georg Ostrovski David Silver Rémi Munos	4
+	Count-Based Exploration with Neural Density Models	2017	Georg Ostrovski Marc G. Bellemare Aäron van den Oord Rémi Munos	4
+	Deep Reinforcement Learning That Matters	2017	Peter Henderson Riashat Islam Philip Bachman Joëlle Pineau Doina Precup David Meger	4
+	Dueling Network Architectures for Deep Reinforcement Learning	2015	Ziyu Wang Tom Schaul Matteo Hessel Hado van Hasselt Marc Lanctot Nando de Freitas	4
+	Go-Explore: a New Approach for Hard-Exploration Problems	2019	Adrien Ecoffet Joost Huizinga Joel Lehman Kenneth O. Stanley Jeff Clune	4
+	Representation Learning with Contrastive Predictive Coding	2018	Aäron van den Oord Yazhe Li Oriol Vinyals	4
+	Implicit Quantile Networks for Distributional Reinforcement Learning	2018	Will Dabney Georg Ostrovski David Silver Rémi Munos	4
+	Deep Reinforcement Learning and the Deadly Triad	2018	Hado van Hasselt Yotam Doron Florian Strub Matteo Hessel Nicolas Sonnerat Joseph Modayil	4
+ PDF Chat	Distributional Reinforcement Learning With Quantile Regression	2018	Will Dabney Mark Rowland Marc G. Bellemare Rémi Munos	4
+	Domain-Independent Optimistic Initialization for Reinforcement Learning	2014	Marlos C. Machado Sriram Srinivasan Michael Bowling	3
+ PDF Chat	PAC Bounds for Discounted MDPs	2012	Tor Lattimore Marcus Hütter	3
+	Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control	2017	Riashat Islam Peter Henderson Maziar Gomrokchi Doina Precup	3
+	Count-Based Exploration in Feature Space for Reinforcement Learning	2017	Jarryd Martin Suraj Narayanan S. Tom Everitt Marcus Hütter	3
+	Curiosity-driven Exploration by Self-supervised Prediction	2017	Deepak Pathak Pulkit Agrawal Alexei A. Efros Trevor Darrell	3
+	The Laplacian in RL: Learning Representations with Efficient Approximations	2018	Yifan Wu George Tucker Ofir Nachum	3
+	Near Optimal Behavior via Approximate State Abstraction	2017	David Abel D Ellis Hershkowitz Michael L. Littman	3
+	The Value Function Polytope in Reinforcement Learning	2019	Robert Dadashi Adrien Ali Taïga Nicolas Le Roux Dale Schuurmans Marc G. Bellemare	3
+	VIME: Variational Information Maximizing Exploration	2016	Rein Houthooft Xi Chen Yan Duan John Schulman Filip De Turck Pieter Abbeel	3
+	Graying the black box: Understanding DQNs	2016	Tom Zahavy Nir Ben Zrihem Shie Mannor	3
+	A Comparative Analysis of Expected and Distributional Reinforcement Learning	2019	Clare Lyle Pablo Samuel Castro Marc G. Bellemare	3
+	FeUdal Networks for Hierarchical Reinforcement Learning	2017	Alexander Sasha Vezhnevets Simon Osindero Tom Schaul Nicolas Heess Max Jaderberg David Silver Koray Kavukcuoglu	3
+	Hyperbolic Discounting and Learning over Multiple Horizons	2019	William Fedus Carles Gelada Yoshua Bengio Marc G. Bellemare Hugo Larochelle	3
+	The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning	2017	Audrūnas Gruslys Will Dabney Mohammad Gheshlaghi Azar Bilal Piot Marc G. Bellemare Rémi Munos	3