GOOGLE RESEARCH – TEL-AVIV
Will lecture on
Between Online Learning and Reinforcement Learning
In this talk I will describe some of my work around Online Learning and Reinforcement Learning.
Online Learning is a classic sub-domain of Machine Learning that has provided endless contributions to fields such as Statistical Learning, Optimization, Decision Making and others.
Unlike Reinforcement Learning which focuses on planning long-term decisions in the face of a non-adversarial environment, Online Learning focuses on making short-term decisions in the face of an adversary - and doing so efficiently.
In my research, therefore, I am interested in the potential interface between these fields, looking to design efficient algorithms for long-term decision making in the face of a possibly adversarial environment---a problem with many real-life use cases.
I will focus on two works that leverage efficient Online Learning algorithms for learning Linear Quadratic Regulators and Stochastic Shortest Path problems.