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.
Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/5978 |
Date | 01 October 1991 |
Creators | Mataric, Maja |
Source Sets | M.I.T. Theses and Dissertation |
Language | en_US |
Detected Language | English |
Format | 13 p., 1444645 bytes, 1130480 bytes, application/postscript, application/pdf |
Relation | AIM-1322 |
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