2026-09-19T11:17:03Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/356422022-01-13T07:54:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Daniela Pucci de Farias.
Schwartz, Jeremy (Jeremy D.)
Massachusetts Institute of Technology. Dept. of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2007-01-10T16:54:48Z
2007-01-10T16:54:48Z
2006
2006
http://hdl.handle.net/1721.1/35642
76704285
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.
Includes bibliographical references (p. 121).
This paper discusses a modification to the Exploration-Exploitation Experts algorithm - (EEE). The EEE is a generalization of the standard experts algorithm which is designed for use in reactive environments. In these problems, the algorithm is only able to learn about the expert that it follows at any given stage. As a result, the convergence rate of the algorithm is heavily dependent on the number of experts which it must consider. We adapt this algorithm for use with a very large set of experts. We do this by capitalizing on the fact that when a set of experts is large, many experts in the set tend to display similarities in behavior. We quantify this similarity with a concept called correlation, and use this correlation information to improve the convergence rate of the algorithm with respect to the number of experts. Experimental results show that given the proper conditions, the convergence rate of the modified algorithm can be independent of the size of the expert space.
by Jeremy Schwartz.
S.M.
121 p.
4242942 bytes
4247979 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
A modified experts algorithm : using correlation to speed convergence with very large sets of experts
Thesis
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