TalkRL: The Reinforcement Learning Podcast

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,...

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 D...

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,...

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 N...

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 Scholar pyPhot...

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 Stefa...

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...

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 MIL...

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...

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 I...

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 N...

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 Georg...

Posters and Hallway episodes are short interviews and poster summaries.  Recorded at RLC 2024 in Amherst MA.    Featuring:   0:01  David Abel from DeepMind on 3 Dogmas of RL   0:55 Kevin Wang from Brown on learning variable depth search for MCTS   2:17 Ashwin Kumar from Washington University in St Louis on fairness in resource allocation   3:36 Prabhat Nagarajan from UAlberta on Value overestimati...

Posters and Hallway episodes are short interviews and poster summaries.  Recorded at RLC 2024 in Amherst MA.   Featuring:   0:01 Kris De Asis from Openmind on Time Discretization   2:23 Anna Hakhverdyan from U of Alberta on Online Hyperparameters   3:59 Dilip Arumugam from Princeton on Information Theory and Exploration   5:04 Micah Carroll from UC Berkeley on Changing preferences and AI alignment...

Posters and Hallway episodes are short interviews and poster summaries.  Recorded at RLC 2024 in Amherst MA.   Featuring:   0:01 Hector Kohler from Centre Inria de l'Université de Lille with " Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning "   2:29 Quentin Delfosse from TU Darmstadt on " Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents "  ...

Posters and Hallway episodes are short interviews and poster summaries.  Recorded at RLC 2024 in Amherst MA.   Featuring:   0:01 Ann Huang from Harvard on Learning Dynamics and the Geometry of Neural Dynamics in Recurrent Neural Controllers   1:37 Jannis Blüml from TU Darmstadt on HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning   3:13 Benjamin Fuhrer from NVI...

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...

Thanks to Professor Silver for permission to record this discussion after his RLC 2024 keynote lecture.    Recorded at UMass Amherst during RCL 2024. Due to the live recording environment, audio quality varies.  We publish this audio in its raw form to preserve the authenticity and immediacy of the discussion.    References   AlphaProof announcement on DeepMind's blog Discovering Reinforcement Lea...

David Silver is a principal research scientist at DeepMind and a professor at University College London.  This interview was recorded at UMass Amherst during RLC 2024.    References   Discovering Reinforcement Learning Algorithms , Oh et al  -- His keynote at RLC 2024 referred to more recent update to this work, yet to be published   Mastering Chess and Shogi by Self-Play with a General Reinforcem...

Dr. Vincent Moens is an Applied Machine Learning Research Scientist at Meta, and an author of TorchRL and TensorDict in pytorch.  Featured References TorchRL: A data-driven decision-making library for PyTorch Albert Bou, Matteo Bettini, Sebastian Dittert, Vikash Kumar, Shagun Sodhani, Xiaomeng Yang, Gianni De Fabritiis, Vincent Moens  Additional References   TorchRL on github   TensorDict Document...

Arash Ahmadian is a Researcher at Cohere and Cohere For AI focussed on Preference Training of large language models. He’s also a researcher at the Vector Institute of AI. Featured Reference Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, Sara...

Glen Berseth is an assistant professor at the Université de Montréal, a core academic member of the Mila - Quebec AI Institute, a Canada CIFAR AI chair, member l'Institute Courtios, and co-director of the Robotics and Embodied AI Lab (REAL).  Featured Links  Reinforcement Learning Conference   Closing the Gap between TD Learning and Supervised Learning--A Generalisation Point of View Raj Ghugare,...

Ian Osband is a Research scientist at OpenAI (ex DeepMind, Stanford) working on decision making under uncertainty.   We spoke about:  - Information theory and RL  - Exploration, epistemic uncertainty and joint predictions  - Epistemic Neural Networks and scaling to LLMs  Featured References  Reinforcement Learning, Bit by Bit   Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi,...