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TalkRL: The Reinforcement Learning PodcastTalkRL podcast is All Reinforcement Learning, All the Time. Author: Robin Ranjit Singh Chauhan
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. Language: en Genres: Technology Contact email: Get it Feed URL: Get it iTunes ID: Get it Trailer: |
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Thomas Frost on Clinical RL with Natural Timings
Episode 74
Friday, 14 August, 2026
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 ReferencesRobust Real-Time Mortality Prediction in the Intensive Care Unit using Temporal Difference Learning Thomas Frost, Kezhi Li, Steve Harris — ML4H Symposium, PMLR 259, 2025Insulin4RL: 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, 2026Insulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning Thomas Frost, Steve Harris — arXiv preprint, 2026Additional ReferencesThe 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
