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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. 2018Off by a beat: the effects of temporal misalignment in reinforcement learning for sepsis treatment — Tang et al. 2026Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment — Zhang et al. 2021Where do doctors disagree? Characterizing Decision Points for Safe Reinforcement Learning in Choosing Vasopressor Treatment — Brown et al. 2025Loss 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 ModelsKevin 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 ReferencesLecture 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 ScholarCornelius Braun on Google ScholarReinforcement 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 ResearchMitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functionsAyush Jain, Norio Kosaka, Xinhu Li, Kyung-Min Kim, Erdem Biyik, Joseph J Lim Applications of Reinforcement LearningWOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management StrategiesWilliam Solow, Sandhya Saisubramanian, Alan Fern Emerging Topics in Reinforcement LearningTowards Improving Reward Design in RL: A Reward Alignment Metric for RL PractitionersCalarina Muslimani, Kerrick Johnstonbaugh, Suyog Chandramouli, Serena Booth, W. Bradley Knox, Matthew E. Taylor Scientific Understanding in Reinforcement LearningMulti-Task Reinforcement Learning Enables Parameter ScalingReginald McLean, Evangelos Chatzaroulas, J K Terry, Isaac Woungang, Nariman Farsad, Pablo Samuel Castro

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  Scientific Understanding in Reinforcement Learning  How Should We Meta-Learn Reinforcement Learning Algorithms?  Alexander David Goldie, Zilin Wang, Jakob Nicolaus Foerster, Shimon Whiteson  Tooling, Environments, and Evaluation for Reinforcement Learning  Syllabus: Portable Curricula for Reinforcement Learning Agents  Ryan Sullivan, Ryan Pégoud, Ameen Ur Rehman, Xinchen Yang, Junyun Huang, Aayush Verma, Nistha Mitra, John P Dickerson  Resourcefulness in Reinforcement Learning  PufferLib 2.0: Reinforcement Learning at 1M steps/s  Joseph Suarez  Theory of Reinforcement Learning  Deep Reinforcement Learning with Gradient  Eligibility Traces  Esraa Elelimy, Brett Daley, Andrew Patterson, Marlos C. Machado, Adam White, Martha White

Prof Thomas Akam is a Neuroscientist at the Oxford University Department of Experimental Psychology.  He is a Wellcome Career Development Fellow and Associate Professor at the University of Oxford, and leads the Cognitive Circuits research group. Featured References Brain Architecture for Adaptive BehaviourThomas Akam, RLDM 2025 Tutorial Additional References Thomas Akam on Google ScholarpyPhotometry : Open source, Python based, fiber photometry data acquisition pyControl : Open source, Python based, behavioural experiment control.Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control, Nathaniel D Daw, Yael Niv, Peter Dayan, 2005Further analysis of the hippocampal amnesic syndrome: 14-year follow-up study of H. M., Milner, B., Corkin, S., & Teuber, H. L., 1968Internally generated cell assembly sequences in the rat hippocampus, Pastalkova E, Itskov V, Amarasingham A, Buzsáki G. Science. 2008Multi-disciplinary Conference on Reinforcement Learning and Decision 2025

Stefano V. Albrecht was previously Associate Professor at the University of Edinburgh, and is currently serving as Director of AI at startup Deepflow. He is a Program Chair of RLDM 2025 and is co-author of the MIT Press textbook "Multi-Agent Reinforcement Learning: Foundations and Modern Approaches". Featured References Multi-Agent Reinforcement Learning: Foundations and Modern Approaches Stefano V. Albrecht,  Filippos Christianos,  Lukas Schäfer MIT Press, 2024 RLDM 2025: Reinforcement Learning and Decision Making Conference Dublin, Ireland EPyMARL: Extended Python MARL framework https://github.com/uoe-agents/epymarl Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks Georgios Papoudakis and Filippos Christianos and Lukas Schäfer and Stefano V. Albrecht

Professor Satinder Singh of Google DeepMind and U of Michigan is co-founder of RLDM.  Here he narrates the origin story of the Reinforcement Learning and Decision Making meeting (not conference). Recorded on location at Trinity College Dublin, Ireland during RLDM 2025. Featured ReferencesRLDM 2025: Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM)June 11-14, 2025 at Trinity College Dublin, Ireland Satinder Singh on Google Scholar

Posters and Hallway episodes are short interviews and poster summaries.  Recorded at NeurIPS 2024 in Vancouver BC Canada.    Featuring   Claire Bizon Monroc from Inria: WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control  Andrew Wagenmaker from UC Berkeley: Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL  Harley Wiltzer from MILA: Foundations of Multivariate Distributional Reinforcement Learning  Vinzenz Thoma from ETH AI Center: Contextual Bilevel Reinforcement Learning for Incentive Alignment  Haozhe (Tony) Chen & Ang (Leon) Li from Columbia: QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers

Posters and Hallway episodes are short interviews and poster summaries.  Recorded at NeurIPS 2024 in Vancouver BC Canada.    Featuring   Jonathan Cook from University of Oxford: Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning  Yifei Zhou from Berkeley AI Research: DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning  Rory Young from University of Glasgow: Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach  Glen Berseth from MILA: Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn  Alexander Rutherford from University of Oxford: JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Posters and Hallway episodes are short interviews and poster summaries.  Recorded at NeurIPS 2024 in Vancouver BC Canada.    Featuring   Jiaheng Hu of University of Texas: Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning  Skander Moalla of EPFL: No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO  Adil Zouitine of IRT Saint Exupery/Hugging Face : Time-Constrained Robust MDPs  Soumyendu Sarkar of HP Labs : SustainDC: Benchmarking for Sustainable Data Center Control  Matteo Bettini of Cambridge University: BenchMARL: Benchmarking Multi-Agent Reinforcement Learning  Michael Bowling of U Alberta : Beyond Optimism: Exploration With Partially Observable Rewards

Abhishek Naik was a student at University of Alberta and Alberta Machine Intelligence Institute, and he just finished his PhD in reinforcement learning, working with Rich Sutton.  Now he is a postdoc fellow at the National Research Council of Canada, where he does AI research on Space applications.  Featured References  Reinforcement Learning for Continuing Problems Using Average Reward Abhishek Naik Ph.D. dissertation 2024  Reward Centering Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton 2024    Learning and Planning in Average-Reward Markov Decision Processes Yi Wan, Abhishek Naik, Richard S. Sutton 2020  Discounted Reinforcement Learning Is Not an Optimization Problem Abhishek Naik, Roshan Shariff, Niko Yasui, Hengshuai Yao, Richard S. Sutton 2019   Additional References  Explaining dopamine through prediction errors and beyond, Gershman et al 2024 (proposes Differential-TD-like learning mechanism in the brain around Box 4)

What do RL researchers complain about after hours at the bar?  In this "Hot takes" episode, we find out!   Recorded at The Pearl in downtown Vancouver, during the RL meetup after a day of Neurips 2024.   Special thanks to "David Beckham" for the inspiration :)

Posters and Hallway episodes are short interviews and poster summaries.  Recorded at RLC 2024 in Amherst MA.    Featuring:   0:01 David Radke of the Chicago Blackhawks NHL on RL for professional sports  0:56 Abhishek Naik from the National Research Council on Continuing RL and Average Reward  2:42 Daphne Cornelisse from NYU on Autonomous Driving and Multi-Agent RL  08:58 Shray Bansal from Georgia Tech on Cognitive Bias for Human AI Ad hoc Teamwork  10:21 Claas Voelcker from University of Toronto on Can we hop in general?  11:23 Brent Venable from The Institute for Human & Machine Cognition on Cooperative information dissemination