Portrait de Amir-massoud Farahmand

Amir-massoud Farahmand

Membre académique principal
farahmand@mila.quebec
Professeur associé, Polytechnique Montréal
University of Toronto
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
Raisonnement
Théorie de l'apprentissage automatique
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Google Scholar
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Biographie

Amir-massoud Farahmand est professeur associé au Département de génie informatique et logiciel de Polytechnique Montréal et membre académique principal à Mila - Institut québécois d'intelligence artificielle, ainsi que professeur associé (statut uniquement) au Département d'informatique de l'Université de Toronto. Il a été chercheur scientifique et titulaire de la chaire CIFAR AI au Vector Institute de Toronto entre 2018-2024, et chercheur principal aux Mitsubishi Electric Research Laboratories (MERL) à Cambridge, aux États-Unis, entre 2014-2018. Il a obtenu son doctorat à l'Université de l'Alberta en 2011, suivi de bourses postdoctorales à l'Université McGill (2011-2014) et à l'Université Carnegie Mellon (CMU) (2014).

La vision de recherche d'Amir-massoud est de comprendre les mécanismes informatiques et statistiques nécessaires pour concevoir des agents d'intelligence artificielle efficaces qui interagissent avec leur environnement et améliorent de manière adaptative leur performance à long terme. Il a de l'expérience dans le développement de méthodes d'apprentissage par renforcement et d'apprentissage automatique pour résoudre des problèmes industriels.

Étudiants actuels

Nimrod De La Vega
Doctorat
Site web
Navid Ghassemi
Collaborateur·rice de recherche - McGill University
Tyler Kastner
Collaborateur·rice de recherche - University of Toronto
Pouya Lahabi
Collaborateur·rice de recherche - Polytechnique
Github
Google Scholar
Thomas Mousseau
Maîtrise recherche - Polytechnique
Site web
Github

Publications

Dissecting Deep RL with High Update Ratios: Combatting Value Divergence.
Marcel Hussing
Claas Voelcker
Igor Gilitschenski
Amir-massoud Farahmand
Eric R. Eaton
2023-12-31
RLJ (publié)
arxiv.org
arxiv.org
PID Accelerated Temporal Difference Algorithms
Mark Bedaywi
Amin Rakhsha
Amir-massoud Farahmand
Long-horizon tasks, which have a large discount factor, pose a challenge for most conventional reinforcement learning (RL) algorithms. Algor… (voir plus)ithms such as Value Iteration and Temporal Difference (TD) learning have a slow convergence rate and become inefficient in these tasks. When the transition distributions are given, PID VI was recently introduced to accelerate the convergence of Value Iteration using ideas from control theory. Inspired by this, we introduce PID TD Learning and PID Q-Learning algorithms for the RL setting, in which only samples from the environment are available. We give a theoretical analysis of the convergence of PID TD Learning and its acceleration compared to the conventional TD Learning. We also introduce a method for adapting PID gains in the presence of noise and empirically verify its effectiveness.
2023-12-31
RLJ (publié)
doi.org
arxiv.org
When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning
Claas Voelcker
Tyler Kastner
Igor Gilitschenski
Amir-massoud Farahmand
We investigate the impact of auxiliary learning tasks such as observation reconstruction and latent self-prediction on the representation le… (voir plus)arning problem in reinforcement learning. We also study how they interact with distractions and observation functions in the MDP. We provide a theoretical analysis of the learning dynamics of observation reconstruction, latent self-prediction, and TD learning in the presence of distractions and observation functions under linear model assumptions. With this formalization, we are able to explain why latent-self prediction is a helpful \emph{auxiliary task}, while observation reconstruction can provide more useful features when used in isolation. Our empirical analysis shows that the insights obtained from our learning dynamics framework predicts the behavior of these loss functions beyond the linear model assumption in non-linear neural networks. This reinforces the usefulness of the linear model framework not only for theoretical analysis, but also practical benefit for applied problems.
2023-12-31
RLJ (publié)
doi.org
arxiv.org