← Authors

Marc G. Bellemare

25 papers · 2015–2024 · 7 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (9) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (30) 🧬 Topic Evolution πŸ† Keyword Champion (3) πŸ”¬ Deep Specialist (14) πŸ† Grand Slam πŸ“ˆ Trend Setter ⚑ Prolific Year (5) πŸ’Ž Century Club (25) πŸ—ƒοΈ Keyword Collector (108) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (8)

Conferences

ICML (9) AAAI (6) AISTATS (4) IJCAI (3) ICLR (1) JMLR (1) NIPS (1)

Top co-authors

RΓ©mi Munos (5) Will Dabney (4) Pablo Samuel Castro (4) Mark Rowland (4) Marlos C. Machado (4) Michael Bowling (3) Robert Dadashi (3) Charline Le Lan (3) Doina Precup (2) Saurabh Kumar (2)

Keywords

reinforcement learning (7) distributional reinforcement learning (7) representation learning (6) deep reinforcement learning (6) value function (5) convergence guarantee (3) return distribution (3) markov decision process (3) count-based exploration (2) exploration bonus (2) successor representation (2) function approximation (2) convergence analysis (2) auxiliary task (2) atari game (2) policy optimization (2) policy iteration (2) state representation (2) sample complexity (1) policy evaluation (1)

Papers

Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning NIPS 2024
An Analysis of Quantile Temporal-Difference Learning JMLR 2024
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces AISTATS 2023
On the Generalization of Representations in Reinforcement Learning AISTATS 2022
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning ICML 2022
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning AAAI 2021
Metrics and Continuity in Reinforcement Learning AAAI 2021
On Bonus Based Exploration Methods In The Arcade Learning Environment ICLR 2020
Representations for Stable Off-Policy Reinforcement Learning ICML 2020
Count-Based Exploration with the Successor Representation AAAI 2020
Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction AAAI 2020
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms AISTATS 2020
A Comparative Analysis of Expected and Distributional Reinforcement Learning AAAI 2019
Distributional reinforcement learning with linear function approximation AISTATS 2019
The Value Function Polytope in Reinforcement Learning ICML 2019
DeepMDP: Learning Continuous Latent Space Models for Representation Learning ICML 2019
Statistics and Samples in Distributional Reinforcement Learning ICML 2019
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents IJCAI 2019
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift AAAI 2019
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (Extended Abstract) IJCAI 2018
Automated Curriculum Learning for Neural Networks ICML 2017
A Laplacian Framework for Option Discovery in Reinforcement Learning ICML 2017
Count-Based Exploration with Neural Density Models ICML 2017
A Distributional Perspective on Reinforcement Learning ICML 2017
Count-Based Frequency Estimation with Bounded Memory IJCAI 2015