Foundations of Reinforcement Learning and Interactive Decision Making
Dylan J. Foster Alexander Rakhlin
These lecture notes give a statistical perspective on the foundations of
reinforcement learning and interactive decision making. We present a unifying
framework for addressing the exploration-exploitation dilemma using frequentist
and Bayesian approaches, with connections and parallels between supervised
learning/estimation and decision making as an overarching theme. Special
attention is paid to function approximation and flexible model classes such as
neural networks. Topics covered include multi-armed and contextual bandits,
structured bandits, and reinforcement learning with high-dimensional feedback.