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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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