Sequential methods underpin many of the most powerful learning techniques, such as reinforcement learning, multi-armed bandits, online convex optimization, and active learning. Although many practical algorithms have been developed for sequential learning, there is a strong need to develop theoretical foundations and to understand fundamental limits. Herein lies an excellent opportunity for information theory to provide answers given its vast arsenal of versatile techniques. At the same time, sequential learning has already started to motivate new problems and insights in information theory and has led to new perspectives. This special issue seeks to fertilize new topics at the intersection of information theory and sequential, active, and reinforcement learning, promoting synergy along the way.