Control theory and reinforcement learning converge on a shared objective: facilitating autonomous, real-time decision-making to optimize dynamical processes. Historically, these disciplines have diverged in assumptions regarding available prior information and in analytical techniques applied. However, recent advances bridging the two domains are fostering collaborations. As part of a
research semester on Control Theory and Reinforcement Learning at CWI, Amsterdam, NL, we have a workshop on broad themes across these topics.