ACM has named Andrew G. Barto and Richard S. Sutton as the recipients of the 2024 ACM A.M. Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning. In a series of papers beginning in the 1980s, Barto and Sutton introduced the main ideas, constructed the mathematical foundations, and developed important algorithms for reinforcement learning—one of the most important approaches for creating intelligent systems.

Barto is Professor Emeritus of Information and Computer Sciences at the University of Massachusetts, Amherst. Sutton is a Professor of Computer Science at the University of Alberta, a Research Scientist at Keen Technologies, and a Fellow at Amii (Alberta Machine Intelligence Institute).

The ACM A.M. Turing Award, often referred to as the “Nobel Prize in Computing,” carries a $1 million prize with financial support provided by Google, Inc. The award is named for Alan M. Turing, the British mathematician who articulated the mathematical foundations of computing.

What is Reinforcement Learning?

The field of artificial intelligence (AI) is generally concerned with constructing agents—that is, entities that perceive and act. More intelligent agents are those that choose better courses of action. Therefore, the notion that some courses of action are better than others is central to AI. Reward—a term borrowed from psychology and neuroscience—denotes a signal provided to an agent related to the quality of its behavior. Reinforcement learning (RL) is the process of learning to behave more successfully given this signal.

The idea of learning from reward has been familiar to animal trainers for thousands of years. Later, Alan Turing’s 1950 paper “Computing Machinery and Intelligence,” addressed the question “Can machines think?” and proposed an approach to machine learning based on rewards and punishments.