Reference number : GC-2017-Q1-00498, amendment number : , agreement type : G, recipient legal name : McLeod, Robert (University of Manitoba)|McLeod, Robert (Université du Manitoba), recipient country : CA, recipient province : nan, recipient city : Manitoba, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The proposed Engage grant will explore the utility of Bayesian Reinforcement Learning (BRL) and integrate it in to the High Performance computing (HPC) framework of Sightline. In general Reinforcement Learning (RL), is a class of machine learning (ML) methods in which a software agent interacts with an unknown environment, with the goal of learning or finding a policy, to optimize some performance metric. Underlying models include Markov Decision Processes and their variants. BRL is typically competitive and unsupervised with the objective of attempting to estimate a random variable X by producing a probability distribution for X. X is inferred through observations or samples of a related random variable Y. Compounding difficulty is noise associated with measurements or samples of Y. Although well-thought-out and well-designed, BRL has not been widely applied or adopted. Many believe that BRL is still in its infancy in spite of demonstrated successes and this aspect is of direct interest to Sightline who are approaching machine learning from the application perspective. It is anticipated that the combination of powerful models for policy approximation such as those associated with deep probabilistic networks in combination with a BRL approach can further facilitate better exploration-exploitation trade-offs associated with policy optimization. This Engage opportunity will provide Sightline with another ML offering to add to its suite of analytics tools and explore the underutilized and untapped potential of BRL.  _x000D_
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_x000D_, program purpose french : The proposed Engage grant will explore the utility of Bayesian Reinforcement Learning (BRL) and integrate it in to the High Performance computing (HPC) framework of Sightline. In general Reinforcement Learning (RL), is a class of machine learning (ML) methods in which a software agent interacts with an unknown environment, with the goal of learning or finding a policy, to optimize some performance metric. Underlying models include Markov Decision Processes and their variants. BRL is typically competitive and unsupervised with the objective of attempting to estimate a random variable X by producing a probability distribution for X. X is inferred through observations or samples of a related random variable Y. Compounding difficulty is noise associated with measurements or samples of Y. Although well-thought-out and well-designed, BRL has not been widely applied or adopted. Many believe that BRL is still in its infancy in spite of demonstrated successes and this aspect is of direct interest to Sightline who are approaching machine learning from the application perspective. It is anticipated that the combination of powerful models for policy approximation such as those associated with deep probabilistic networks in combination with a BRL approach can further facilitate better exploration-exploitation trade-offs associated with policy optimization. This Engage opportunity will provide Sightline with another ML offering to add to its suite of analytics tools and explore the underutilized and untapped potential of BRL.  _x000D_
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_x000D_, agreement title english : Computer vision and deep learning: Bayesian Reinforcement Learning, agreement title french : Computer vision and deep learning: Bayesian Reinforcement Learning, agreement number : EGP, agreement value : 24913.0, agreement start date : 2017-06-14, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada