Portrait of Pablo Samuel Castro

Pablo Samuel Castro

Core Industry Member
pablo-samuel.castro@mila.quebec
Adjunct professor, Université de Montréal, Department of Computer Science and Operations Research
Research Software Developer, Google
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Biography

Pablo Samuel Castro was born and raised in Quito, Ecuador, and moved to Montréal after high school to study at McGill University. For his PhD, he studied reinforcement learning with Doina Precup and Prakash Panangaden at McGill. Castro has been working at Google for over eleven years. He is currently a staff research software developer at Google DeepMind in Montréal, where he conducts fundamental reinforcement learning research and is a regular advocate for increasing LatinX representation in the research community.

He is also an adjunct professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. In addition to his interest in coding, AI and math, Castro is an active musician.

Current Students

Ayoub Echchahed
Master's Research - Université de Montréal
ayoub.echchahed@mila.quebec
Website
Gandharv Patil
PhD - McGill University
Principal supervisor :
Doina Precup
patilgan@mila.quebec
Website
Github
Google Scholar
Johan Samir Obando Ceron
PhD - Université de Montréal
Principal supervisor :
Aaron Courville
johan.ceron@mila.quebec
Github
Google Scholar
Samuel Garcin
Collaborating researcher
samuel.garcin@mila.quebec
Website
Github
Google Scholar

Publications

Dopamine: A Research Framework for Deep Reinforcement Learning
Pablo Samuel Castro
Subhodeep Moitra
Carles Gelada
Saurabh Kumar
Marc Gendron-Bellemare
Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provid… (see more)e stable, comprehensive implementations for benchmarking. At the same time, recent deep RL research has become more diverse in its goals. In this paper we introduce Dopamine, a new research framework for deep RL that aims to support some of that diversity. Dopamine is open-source, TensorFlow-based, and provides compact and reliable implementations of some state-of-the-art deep RL agents. We complement this offering with a taxonomy of the different research objectives in deep RL research. While by no means exhaustive, our analysis highlights the heterogeneity of research in the field, and the value of frameworks such as ours.
2018-09-27
ArXiv (preprint)
arxiv.org
arxiv.org