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Journal of Artificial Intelligence Research, 16 (2002) 59-104. Submitted 5/01; published 2/02
© 2002 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.
Next: 1 Introduction
Chris Drummond
School of Information Technology and Engineering
University of Ottawa, Ontario, Canada, K1N 6N5
cdrummon@site.uottawa.ca
This paper discusses a system that accelerates reinforcement learning by using transfer from related tasks. Without such transfer, even if two tasks are very similar at some abstract level, an extensive re-learning effort is required. The system achieves much of its power by transferring parts of previously learned solutions rather than a single complete solution. The system exploits strong features in the multi-dimensional function produced by reinforcement learning in solving a particular task. These features are stable and easy to recognize early in the learning process. They generate a partitioning of the state space and thus the function. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. Experiments demonstrate that function composition often produces more than an order of magnitude increase in learning rate compared to a basic reinforcement learning algorithm.