Richard S. Sutton - Webdocs Cs Ualberta - University of Alberta
Richard S. Sutton - Webdocs Cs Ualberta - University of Alberta
Richard S. Sutton - Webdocs Cs Ualberta - University of Alberta
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110.Precup, D., <strong>Sutton</strong>, R. S., “Empirical comparison <strong>of</strong> gradient descent and exponentiated<br />
gradient descent in supervised and reinforcement learning.” Technical Report UM-<br />
CS-1996-070, Department <strong>of</strong> Computer Science, <strong>University</strong> <strong>of</strong> Massachusetts, Amherst,<br />
MA 01003, 1996.<br />
111.<strong>Sutton</strong>, R. S., “On the virtues <strong>of</strong> linear learning and trajectory distributions.” Abstract in<br />
Proceedings <strong>of</strong> the Workshop on Value Function Approximation, Conference on Neural<br />
Information Processing Systems, 1995.<br />
112.<strong>Sutton</strong>, R. S., “Machines that learn and mimic the brain.” In ACCESS, GTE's Journal <strong>of</strong><br />
Science and Technology, 1992. Reprinted in Stethoscope Quarterly, Spring 1993.<br />
113.<strong>Sutton</strong>, R. S., “The challenge <strong>of</strong> reinforcement learning.” Introduction to a special issue<br />
on reinforcement learning, Machine Learning 8, No 3/4, pp. 225–227, 1992.<br />
114.<strong>Sutton</strong>, R. S., “Implementation details <strong>of</strong> the TD(lambda) procedure for the case <strong>of</strong><br />
vector predictions and backpropagation.” GTE Laboratories Technical Report<br />
TR87-509.1, GTE Laboratories, 40 Sylvan Road, Waltham, MA 02254, 1989.<br />
115.<strong>Sutton</strong>, R. S., “NADALINE: a normalized adaptive linear element that learns<br />
efficiently.” GTE Laboratories Technical Report TR88-509.4, GTE Laboratories, 40<br />
Sylvan Road, Waltham, MA 02254, 1988.<br />
116.<strong>Sutton</strong>, R. S., “Temporal credit assignment in reinforcement learning,” Ph.D.<br />
dissertation, <strong>University</strong> <strong>of</strong> Massachusetts, Amherst, MA. Published as COINS Technical<br />
Report 84-2, 1984.<br />
117.Barto, A. G., <strong>Sutton</strong>, R. S., “Goal seeking components for adaptive intelligence: an initial<br />
assessment.” Air Force Wright Aeronautical Laboratories Technical Report, AFWAL-<br />
TR-81-1070, Wright-Patterson Air Force Base, Ohio, 1981.<br />
118.<strong>Sutton</strong>, R. S., “Single channel theory: a neuronal theory <strong>of</strong> learning,” Brain Theory<br />
Newsletter 3, No. 3/4, pp. 72–75, 1978. (earliest publication)<br />
Patent<br />
119.<strong>Sutton</strong>, R. S., “Apparatus for Machine Learning.” United States Patent No. 5,946,675,<br />
August 31, 1999. Assigned to GTE Laboratories Incorporated, Waltham, Mass.<br />
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