TalkRL: The Reinforcement Learning Podcast

Robin Ranjit Singh Chauhan

5.0（1）
テクノロジー

TalkRL podcast is All Reinforcement Learning, All the Time. In-depth interviews with brilliant people at the forefront of RL research and practice. Guests from places like MILA, OpenAI, MIT, DeepMind, Berkeley, Amii, Oxford, Google Research, Brown, Waymo, Caltech, and Vector Institute. Hosted by Robin Ranjit Singh Chauhan.

2025/11/10

Danijar Hafner on Dreamer v4

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

1時間41分
2025/09/08

David Abel on the Science of Agency @ RLDM 2025

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

1時間
2025/08/19

Jake Beck, Alex Goldie, & Cornelius Braun on Sutton's OaK, Metalearning, LLMs, Squirrels @ RLC 2025

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

12分
2025/08/18

Outstanding Paper Award Winners - 2/2 @ RLC 2025

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

14分
2025/08/15

Outstanding Paper Award Winners - 1/2 @ RLC 2025

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

7分
2025/08/04

Thomas Akam on Model-based RL in the Brain

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

52分
2025/07/22

Stefano Albrecht on Multi-Agent RL @ RLDM 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

32分
2025/06/25

Satinder Singh: The Origin Story of RLDM @ RLDM 2025

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

6分

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予告編

About TalkRL Podcast: All Reinforcement Learning, All the Time

August 2, 2019 Transcript The idea with TalkRL Podcast is to hear from brilliant folks from across the world of Reinforcement Learning, both research and applications. As much as possible, I want to hear from them in their own language. I try to get to know as much as I can about their work before hand. And Im not here to convert anyone, I want to reach people who are already into RL. So we wont stop to explain what a value function is, for example. Though we also wont assume everyone has read the very latest papers. Why am I doing this? Because it’s a great way to learn from the most inspiring people in the field! There’s so much happening in the universe of RL, and there’s tons of interesting angles and so many fascinating minds to learn from. Now I know there is no shortage of books, papers, and lectures, but so much goes unsaid. I mean I guess if you work at MILA or AMII or Vector Institute, you might be having these conversations over coffee all the time, but I live in a little village in the woods in BC, so for me, these remote interviews are like a great way to have these conversations, and I hope sharing with the community makes it more worthwhile for everyone. In terms of format, the first 2 episodes were interviews in longer form, around an hour long. Going forward, some may be a lot shorter, it depends on the guest. If you want want to be a guest or suggest a guest, goto talkrl.com/about, you will find a link to a suggestion form. Thanks for listening!

2019/08/01

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2分

TalkRL podcast is All Reinforcement Learning, All the Time. In-depth interviews with brilliant people at the forefront of RL research and practice. Guests from places like MILA, OpenAI, MIT, DeepMind, Berkeley, Amii, Oxford, Google Research, Brown, Waymo, Caltech, and Vector Institute. Hosted by Robin Ranjit Singh Chauhan.

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Robin Ranjit Singh Chauhan
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19年 - 25年
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73
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不適切な内容を含まない
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© 2026 Robin Ranjit Singh Chauhan
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TalkRL: The Reinforcement Learning Podcast

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