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

Robust Real-Time Mortality Prediction in the Intensive Care Unit using Temporal Difference Learning
Thomas Frost, Kezhi Li, Steve Harris — ML4H Symposium, PMLR 259, 2025

Insulin4RL: Real-Time Insulin Infusions for Offline Reinforcement Learning
Thomas Frost, Steve Harris — PhysioNet, 2026 (RRID:SCR_007345)

The Hidden Risks of Temporal Resampling in Clinical Reinforcement Learning
Thomas Frost, Hrisheekesh Vaidya, Steve Harris — arXiv preprint, 2026

Insulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning
Thomas Frost, Steve Harris — arXiv preprint, 2026

Additional References
The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care — Komorowski et al. 2018
Off by a beat: the effects of temporal misalignment in reinforcement learning for sepsis treatment — Tang et al. 2026
Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment — Zhang et al. 2021
Where do doctors disagree? Characterizing Decision Points for Safe Reinforcement Learning in Choosing Vasopressor Treatment — Brown et al. 2025
Loss of plasticity in deep continual learning — Dohare et al. 2024

Danijar Hafner was a Research Scientist at Google DeepMind until recently.

Featured References   
Training Agents Inside of Scalable World Models [ blog ] 
Danijar Hafner, Wilson Yan, Timothy Lillicrap
One Step Diffusion via Shortcut Models
Kevin Frans, Danijar Hafner, Sergey Levine, Pieter Abbeel
Action and Perception as Divergence Minimization [ blog ] 
Danijar Hafner, Pedro A. Ortega, Jimmy Ba, Thomas Parr, Karl Friston, Nicolas Heess 

Additional References   
Mastering Diverse Domains through World Models [ blog ] DreaverV3l Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap   
Mastering Atari with Discrete World Models [ blog ] DreaverV2 ; Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba   
Dream to Control: Learning Behaviors by Latent Imagination [ blog ] Dreamer ; Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi 
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos [ Blog Post ], Baker et al

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.  

Featured References  

Plasticity as the Mirror of Empowerment  
David Abel, Michael Bowling, André Barreto, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh  

A Definition of Continual RL  
David Abel, André Barreto, Benjamin Van Roy, Doina Precup, Hado van Hasselt, Satinder Singh  

Agency is Frame-Dependent  
David Abel, André Barreto, Michael Bowling, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh  

On the Expressivity of Markov Reward  
David Abel, Will Dabney, Anna Harutyunyan, Mark Ho, Michael Littman, Doina Precup, Satinder Singh — Outstanding Paper Award, NeurIPS 2021  

Additional References  
Bidirectional Communication Theory — Marko 1973  
Causality, Feedback and Directed Information — Massey 1990  
The Big World Hypothesis — Javed et al. 2024  
Loss of plasticity in deep continual learning — Dohare et al. 2024  
Three Dogmas of Reinforcement Learning — Abel 2024  
Explaining dopamine through prediction errors and beyond — Gershman et al. 2024  
David Abel Google Scholar  
David Abel personal website

Recorded at Reinforcement Learning Conference 2025 at University of Alberta, Edmonton Alberta Canada.
Featured References

Lecture on the Oak Architecture, Rich Sutton
Alberta Plan, Rich Sutton with Mike Bowling and Patrick Pilarski

Additional References
Jacob Beck on Google Scholar 
Alex Goldie on Google Scholar
Cornelius Braun on Google Scholar
Reinforcement Learning Conference

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

Empirical Reinforcement Learning Research
Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions
Ayush Jain, Norio Kosaka, Xinhu Li, Kyung-Min Kim, Erdem Biyik, Joseph J Lim
Applications of Reinforcement Learning
WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies
William Solow, Sandhya Saisubramanian, Alan Fern
Emerging Topics in Reinforcement Learning
Towards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners
Calarina Muslimani, Kerrick Johnstonbaugh, Suyog Chandramouli, Serena Booth, W. Bradley Knox, Matthew E. Taylor
Scientific Understanding in Reinforcement Learning
Multi-Task Reinforcement Learning Enables Parameter Scaling
Reginald McLean, Evangelos Chatzaroulas, J K Terry, Isaac Woungang, Nariman Farsad, Pablo Samuel Castro