Show Notes
Max Schwarzer is a PhD student at Mila, with Aaron Courville and Marc Bellemare, interested in RL scaling, representation learning for RL, and RL for science. Max spent the last 1.5 years at Google Brain/DeepMind, and is now at Apple Machine Learning Research. Featured References
Bigger, Better, Faster: Human-level Atari with human-level efficiency Max Schwarzer, Johan Obando-Ceron, Aaron Courville, Marc Bellemare, Rishabh Agarwal, Pablo Samuel Castro
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, Aaron Courville
The Primacy Bias in Deep Reinforcement Learning Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron Courville
Additional References Rainbow: Combining Improvements in Deep Reinforcement Learning, Hessel et al 2017 When to use parametric models in reinforcement learning? Hasselt et al 2019 Data-Efficient Reinforcement Learning with Self-Predictive Representations, Schwarzer et al 2020 Pretraining Representations for Data-Efficient Reinforcement Learning, Schwarzer et al 2021
More from TalkRL: The Reinforcement Learning Podcast
Dr Thomas Frost is an emergency physician based in London, UK. He is also in the final stages of completing a PhD at University College London, where he has been looking at offline reinforcement learning applied to healthcare settings. Featured References
Danijar Hafner was a Research Scientist at Google DeepMind until recently.
David Abel is a Senior Research Scientist at DeepMind on the Agency team, and an Honorary Fellow at the University of Edinburgh. His research blends computer science and philosophy, exploring foundational questions about reinforcement learning, definitions, and the nature of agency.
Recorded at Reinforcement Learning Conference 2025 at University of Alberta, Edmonton Alberta Canada. Featured References
We caught up with the RLC Outstanding Paper award winners for your listening pleasure. Recorded on location at Reinforcement Learning Conference 2025, at University of Alberta, in Edmonton Alberta Canada in August 2025. Featured References