Reinforcement Learning in Continuous Time: The q-Learning Theory
Prof. Xunyu ZhouColumbia University 15:30 – 17:00 鴻經館 M107
Abstract：
Reinforcement learning (RL) is an active subarea in machine learning, and has been developed predominantly for Markov decision processes (MDPs) in discrete time with discrete state and action spaces. In this talk, I will report the recent development of RL theory in continuous time and state space with possibly continuous action space, focusing on the q-learning theory. The theory centers around various martingale conditions that lead naturally to on-policy and off-policy RL algorithms. Time permitting I will also present applications to data-driven portfolio selection.
20230608演講_Prof. Xunyu Zhou (Columbia University)