A review of reinforcement learning in chemistry

Abstract

The growth of machine learning as a tool for research in computational chemistry is well documented. For many years, this growth was heavily driven by the paradigms of supervised and unsupervised learning. Recently, however, there has been increased interest in the use of a third paradigm: reinforcement learning. This approach, in which an agent interacts with an environment to learn which actions it should take to maximise a long-term objective, is particularly suited to problems of planning or sequential decision making. In this review, we present an accessible summary of the theory behind reinforcement learning (and its common extension, deep reinforcement learning) tailored specifically to chemistry researchers. We also review the applications of reinforcement learning which already exist within the world of chemistry, and consider the future direction of research based on this promising technique.