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A Comparative Analysis of Reinforcement Learning Methods

This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapting situated agents. We discuss two RL algorithms: Q-learning and the Bucket Brigade. We introduce a special case of the Bucket Brigade, and analyze and compare its performance to Q in a number of experiments. Next we discuss the key problems of RL: time and space complexity, input generalization, sensitivity to parameter values, and selection of the reinforcement function. We address the tradeoffs between the built-in and learned knowledge and the number of training examples required by a learning algorithm. Finally, we suggest directions for future research.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/5978
Date01 October 1991
CreatorsMataric, Maja
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format13 p., 1444645 bytes, 1130480 bytes, application/postscript, application/pdf
RelationAIM-1322

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