Interesting RL papers

Mastering Diverse Domains through World Models

Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap, 2023 — paper

The Value Function Polytope in Reinforcement Learning

Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare, ICML 2019 — paper

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 — paper

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine, ICML 2018 — paper

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer, Johan Obando-Ceron, Aaron Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro, ICML 2023 — paper

A Distributional Perspective on Reinforcement Learning

Marc G. Bellemare, Will Dabney, Rémi Munos, ICML 2017 — paper

Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Hojoon Lee, Youngdo Lee, Takuma Seno, Donghu Kim, Peter Stone, Jaegul Choo, ICML 2025 — paper

Generative Adversarial Imitation Learning

Jonathan Ho, Stefano Ermon, NeurIPS 2016 — paper

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine, ICLR 2019 — paper

The Primacy Bias in Deep Reinforcement Learning

Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, Aaron Courville, ICML 2022 — paper

The Dormant Neuron Phenomenon in Deep Reinforcement Learning

Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci, ICML 2023 — paper

Advantage Alignment Algorithms

Juan Agustin Duque, Milad Aghajohari, Tim Cooijmans, Razvan Ciuca, Tianyu Zhang, Gauthier Gidel, Aaron Courville, ICLR 2025 — paper

this list will grow. slowly.