TalkRL: The Reinforcement Learning Podcast

Joseph Modayil is the Founder, President & Research Director of Openmind Research Institute. Featured References Openmind Research Institute The Alberta Plan for AI Research Richard S. Sutton, Michael Bowling, Patrick M. Pilarski Additional References Joseph Modayil on Google ScholarJoseph Modayil Homepage

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 ModelsblogMastering Atari with Discrete World ModelsblogDream to Control: Learning Behaviors by Latent ImaginationblogVideo PreTraining (VPT): Learning to Act by Watching Unlabeled Online VideosBlog Post

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 TheoryCausality, Feedback and Directed InformationThe Big World HypothesisLoss of plasticity in deep continual learningThree Dogmas of Reinforcement LearningExplaining dopamine through prediction errors and beyondDavid Abel Google ScholarDavid Abel personal website

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

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 Behaviour Thomas Akam, RLDM 2025 Tutorial Additional References Thomas Akam on Google ScholarUncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral controlFurther analysis of the hippocampal amnesic syndrome: 14-year follow-up study of H. M.Internally generated cell assembly sequences in the rat hippocampusMulti-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 References RLDM 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 WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm ControlOvercoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RLFoundations of Multivariate Distributional Reinforcement LearningContextual Bilevel Reinforcement Learning for Incentive AlignmentQGym: 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 Artificial Generational Intelligence: Cultural Accumulation in Reinforcement LearningDigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement LearningEnhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent ApproachImproving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy ChurnJaxMARL: 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 Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement LearningNo Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPOTime-Constrained Robust MDPsSustainDC: Benchmarking for Sustainable Data Center ControlBenchMARL: Benchmarking Multi-Agent Reinforcement LearningBeyond 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 dissertation 2024 Reward Centering Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutto 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

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: David RadkeAbhishek NaikDaphne CornelisseShray BansalClaas VoelckerBrent Venable

Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: David AbelKevin WangAshwin KumarPrabhat Nagarajan

Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: Kris De AsisAnna HakhverdyanDilip ArumugamMicah Carroll

Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: Interpretable and Editable Programmatic Tree Policies for Reinforcement LearningInterpretable Concept Bottlenecks to Align Reinforcement Learning AgentsUnderstanding biological active sensing behaviors by interpreting learned artificial agent policiesOCAtari: Object-Centric Atari 2600 Reinforcement Learning EnvironmentsResolving Partial Observability in Decision Processes via the Lambda DiscrepancyAgent-Centric Human Demonstrations Train World Models

Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: Ann HuangLearning Dynamics and the Geometry of Neural Dynamics in Recurrent Neural ControllersJannis BlümlHackAtari: Atari Learning Environments for Robust and Continual Reinforcement LearningBenjamin FuhrerGradient Boosting Reinforcement LearningPaul FestorEvaluating the impact of explainable RL on physician decision-making in high-fidelity simulations: insights from eye-tracking metrics

Finale Doshi-Velez is a Professor at the Harvard Paulson School of Engineering and Applied Sciences. This off-the-cuff interview was recorded at UMass Amherst during the workshop day of RL Conference on August 9th 2024. Host notes: I've been a fan of some of Prof Doshi-Velez' past work on clinical RL and hoped to feature her for some time now, so I jumped at the chance to get a few minutes of her thoughts -- even though you can tell I was not prepared and a bit flustered tbh. Thanks to Prof Doshi-Velez for taking a moment for this, and I hope to cross paths in future for a more in depth interview. References Finale Doshi-VelezFinale Doshi-Velez