Barto, Andrew
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Email Address
Birth Date
Job Title
Professor Emeritus, Department of Computer Science, College of Natural Sciences
Last Name
Barto
First Name
Andrew
Discipline
Chemistry
Expertise
Algebraic approaches to abstraction
Computational models of learning and adaptation in animal motor control systems
Interaction of learning and evolution
Mathematical theory of learning and planning in stochastic sequential decision problems
Methods for scaling-up reinforcement learning methods
Psychology, neuroscience, and computational theory of motivation, reward, and addiction
Computational models of learning and adaptation in animal motor control systems
Interaction of learning and evolution
Mathematical theory of learning and planning in stochastic sequential decision problems
Methods for scaling-up reinforcement learning methods
Psychology, neuroscience, and computational theory of motivation, reward, and addiction
Introduction
My program of research is an interdisciplinary study of mechanisms and algorithms permitting systems—both natural and artificial—to improve performance with experience, that is, to learn. I direct the Autonomous Learning Laboratory in the Department of Computer Science, which focuses on learning in both machines and animals. We are a highly interdisciplinary lab, interacting with researchers in psychology, neuroscience, control engineering, operations research, and robotics. We are best known for pioneering work in Reinforcement Learning. This is a framework for learning to maximize reward over time while interacting with a dynamic environment. We also work on neural models of animal motor learning, maintaining close contact with the laboratory of Prof. John Moore, and on the development of motor control abilities by infants in collaboration with Profs. Neil Berthier and Rachel Keen. Biological control systems demonstrate an amazing ability to deal with complex and ever-changing bodies and environments.