Portrait de Pablo Samuel Castro

Pablo Samuel Castro

Membre industriel principal
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Chercheur scientifique, Google DeepMind
Sujets de recherche
Apprentissage par renforcement
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Biographie

Pablo Samuel Castro est né et a grandi à Quito, en Équateur, et a déménagé à Montréal après l'école secondaire pour étudier à l’Université McGill. Il y a obtenu un doctorat en se concentrant sur l'apprentissage par renforcement, sous la supervision de Doina Precup et Prakash Panangaden. Il est chercheur scientifique à Google DeepMind à Montréal. Il s’intéresse particulièrement à la recherche fondamentale sur l'apprentissage par renforcement et plaide régulièrement en faveur d'une augmentation de la représentation des personnes d’origine latino-américaine dans la communauté de recherche. Il est également professeur adjoint au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal. Outre son intérêt pour le codage, l'intelligence artificielle et les mathématiques, Pablo Samuel est un musicien actif.

Étudiants actuels

Publications

Distributional reinforcement learning with linear function approximation
Bellemare Marc-Emmanuel
Nicolas Roux
Pablo Samuel Castro
Subhodeep Moitra
Despite many algorithmic advances, our theoretical understanding of practical distributional reinforcement learning methods remains limited.… (voir plus) One exception is Rowland et al. (2018)'s analysis of the C51 algorithm in terms of the Cramer distance, but their results only apply to the tabular setting and ignore C51's use of a softmax to produce normalized distributions. In this paper we adapt the Cramer distance to deal with arbitrary vectors. From it we derive a new distributional algorithm which is fully Cramer-based and can be combined to linear function approximation, with formal guarantees in the context of policy evaluation. In allowing the model's prediction to be any real vector, we lose the probabilistic interpretation behind the method, but otherwise maintain the appealing properties of distributional approaches. To the best of our knowledge, ours is the first proof of convergence of a distributional algorithm combined with function approximation. Perhaps surprisingly, our results provide evidence that Cramer-based distributional methods may perform worse than directly approximating the value function.
2019-04-10
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
proceedings.mlr.press
Dopamine: A Research Framework for Deep Reinforcement Learning
Pablo Samuel Castro
Subhodeep Moitra
Carles Gelada
Saurabh Kumar
Bellemare Marc-Emmanuel
Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provid… (voir plus)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.
2018-09-26
ArXiv (prépublication)
openreview.net
openreview.net