Andrew G. Barto, an American computer scientist, AI-researcher and Professor of Computer Science, University of Massachusetts, Amherst. His 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 [1] .
Andy Barto [2] [3]

Selected Publications

• Richard Sutton, Andrew Barto (1981). Toward a modern theory of adaptive networks: Expectation and prediction, Psychological Review, Vol. 88, pp. 135-170. pdf
• Richard Sutton, Andrew Barto (1990). Time-Derivative Models of Pavlovian Reinforcement. in Michael Gabriel, John Moore (eds.) (1990). Learning and Computational Neuroscience: Foundations of Adaptive Networks. MIT Press, pdf
• Richard C. Yee, Sharad Saxena, Paul E. Utgoff, Andrew Barto (1990). Explaining Temporal Differences to Create Useful Concepts for Evaluating States. AAAI 1990, pdf
• Steven Bradtke, Andrew Barto (1996) Linear Least-Squares Algorithms for Temporal Difference Learning. Machine Learning, Vol. 22, Nos. 1/2/3, pdf
• Richard Sutton, Andrew Barto (1998). Reinforcement Learning: An Introduction. MIT Press

External Links

• Andrew G. Barto, Department of Computer Science, University of Massachusetts Amherst
• The Mathematics Genealogy Project - Andrew Barto
• Reinforcement Learning: An Introduction ebook by Richard Sutton and Andrew Barto

References

1. ^ Barto biosketch
2. ^ Photo (cropped) from the Multidisciplinary Symposium on Reinforcement Learning (MSRL)
3. ^ Multidisciplinary Symposium on Reinforcement Learning, 2009
4. ^ Rich Sutton's Publications
5. ^ ICGA Reference Database (pdf)
6. ^ Barto Publications since 1994
7. ^ Publications Autonomous Learning Laboratory before 1994

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