Computer vision and deep learning: Bayesian Reinforcement Learning

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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.