Deep Bayesian Reinforcement Learning

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The proposed Engage Plus grant will continue to 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 by producing a probability**distribution for it. Although well-thought-out and well-designed, BRL has not been widely applied or adopted.**BRL is still in relative 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 Plus 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.