Value-Function-Based Transfer for Reinforcement Learning Using Structure Mapping (2006)
Transfer learning concerns applying knowledge learned in one task (the source) to improve learning another related task (the target). In this paper, we use structure mapping, a psychological and computational theory about analogy making, to find mappings between the source and target tasks and thus construct the transfer functional automatically. Our structure mapping algorithm is a specialized and optimized version of the structure mapping engine and uses heuristic search to find the best maximal mapping. The algorithm takes as input the source and target task specifications represented as qualitative dynamic Bayes networks, which do not need probability information. We apply this method to the Keepaway task from RoboCup simulated soccer and compare the result from automated transfer to that from handcoded transfer.
In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 415-20, July 2006.

Yaxin Liu Postdoctoral Alumni
Peter Stone Faculty pstone [at] cs utexas edu