Title: Randomized Impulse Control Problem and Reinforcement Learning

Abstract: In this work, we study a randomized optimal impulse control problem. This framework is particularly suitable for devising reinforcement learning algorithms with exploration to solve the otherwise hard-to-explicitly-derive optimal impulse control policies. We provide a two-fold randomized impulse control framework and then analyze the associated HJB equation. We discuss its relations with (randomized) optimal stopping as well as the classical impulse control problem. We also present an exploratory reinforcement learning algorithm to learn the randomized impulse policy. This is a joint work with Prof. Yuchao Dong (Tongji University) and Zhouhao Yang (Johns Hopkins University).