Andrew G. Barto

Professor Emeritus
Off Campus


Theory and application of methods for learning and planning in stochastic sequential decision problems; algebraic approaches to abstraction; psychology, neuroscience, and computational theory of motivation, reward, and addiction; computational models of learning and adaptation in animal motor control systems.


Professor Barto's research centers on learning in natural and artificial systems, and he has studied machine learning algorithms since 1977, contributing to the development of the computational theory and practice of reinforcement learning. His current research centers on models of motor learning and reinforcement learning methods for real-time planning and control, with specific interest in autonomous mental development through intrinsically motivated reinforcement learning.

Research Centers & Labs: 


Ph.D., Computer Science, University of Michigan (1975), B.S., Mathematics, University of Michigan (1970). Professor Barto joined the College of Information and Computer Sciences of the University of Massachusetts Amherst in 1977 as a Postdoctoral Research Associate, became an Associate Professor in 1982, and has been a Full Professor since 1991. He is co-director of the Autonomous Learning Laboratory and a core faculty member of the Neuroscience and Behavior Program of the University of Massachusetts Amherst. Professor Barto was Department Chair from 2007-2011.

Activities & Awards

Professor Barto serves as an associate editor of Neural Computation, as a member of the editorial boards of the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, Adaptive Behavior, and Theoretical Computer Science-C: Natural Computing. Professor Barto is a Fellow of the American Association for the Advancement of Science, a Fellow of the IEEE, and a member of the American Association for Artificial Intelligence and the Society for Neuroscience. He received the 2004 IEEE Neural Network Society Pioneer Award for contributions to the field of reinforcement learning. He has published over one hundred papers or chapters in journals, books, and conference and workshop proceedings. He is co-author with Richard Sutton of the book "Reinforcement Learning: An Introduction," MIT Press 1998, and co-editor with Jennie Si, Warren Powell, and Don Wunch II of the "Handbook of Learning and Approximate Dynamic Programming," Wiley-IEEE Press, 2004.