Reinforcement learning is a highly active area of research, blending ideas and techniques from control, optimization, machine learning, and computer science. Given this diversity of viewpoints and frameworks, it is imperative to understand their strengths and their limitations. The aim of this iDS2 virtual mini-workshop is a constructive dialogue and exchange of ideas between researchers in these fields. This moderated discussion follows the two tutorial-style talks emphasizing the asymptotic and the non-asymptotic perspectives, and will feature both Sean Meyn (University of Florida) and Elad Hazan (Princeton University).
