EPISODE · Oct 10, 2019 · 56 MIN
Pablo Samuel Castro
from TalkRL: The Reinforcement Learning Podcast · host Robin Ranjit Singh Chauhan
Dr Pablo Samuel Castro is a Staff Research Software Engineer at Google Brain. He is the main author of the Dopamine RL framework. Featured References A Comparative Analysis of Expected and Distributional Reinforcement Learning Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare A Geometric Perspective on Optimal Representations for Reinforcement Learning Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle Dopamine: A Research Framework for Deep Reinforcement Learning Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, Marc G. Bellemare Dopamine RL framework on github Tensorflow Agents on github Additional References Using Linear Programming for Bayesian Exploration in Markov Decision Processes Pablo Samuel Castro, Doina Precup Using bisimulation for policy transfer in MDPs Pablo Samuel Castro, Doina Precup Rainbow: Combining Improvements in Deep Reinforcement Learning Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver Implicit Quantile Networks for Distributional Reinforcement Learning Will Dabney, Georg Ostrovski, David Silver, Rémi Munos A Distributional Perspective on Reinforcement Learning Marc G. Bellemare, Will Dabney, Rémi Munos
What this episode covers
Pablo Samuel Castro drops in and drops knowledge on distributional RL, bisimulation, the Dopamine RL Framework, TF-Agents, and much more!
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Pablo Samuel Castro
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