Andrew Barto

Andrew Gehret Barto is an American computer scientist, born in 1948 and professor emeritus of computer science at the University of Massachusetts Amherst, best known for his foundational contributions to computational reinforcement learning. He took a degree in mathematics with distinction at the University of Michigan in 1970 after first studying naval architecture, and after reading work by Michael Arbib, Warren McCulloch and Walter Pitts he became interested in modelling the brain, completing a PhD on cellular automata five years later. He joined the University of Massachusetts Amherst in 1977, was department chair from 2007 to 2011, and co-directed the Autonomous Learning Laboratory, which produced several key ideas in reinforcement learning. With his doctoral student Richard Sutton he used Markov decision processes as the mathematical foundation for agents that learn in an unknown environment, and the two wrote the book Reinforcement Learning: An Introduction. In 2024 the two received the Turing Award from the Association for Computing Machinery for developing the conceptual and algorithmic foundations of reinforcement learning, and in 2026 the IEEE Frank Rosenblatt Award.