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TalkRL: The Reinforcement Learning Podcast

TalkRL: The Reinforcement Learning Podcast

By: Robin Ranjit Singh Chauhan
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About this listen

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 Robin Ranjit Singh Chauhan
Episodes
  • Joseph Modayil of Openmind Research Institute @ RLC 2025
    Jan 3 2026

    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 Scholar
    • Joseph Modayil Homepage
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    4 mins
  • Danijar Hafner on Dreamer v4
    Nov 10 2025

    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
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    1 hr and 41 mins
  • David Abel on the Science of Agency @ RLDM 2025
    Sep 8 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
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    1 hr
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