Lab Meeting October 13, 2008(Tiffany): Graph Laplacian Based Transfer Learning in Reinforce-ment Learning

Authors:
Yi-Ting Tsao, Ke-Ting Xiao, Von-Wun Soo

Abstract:
The aim of transfer learning is to accelerate learning in related domains. In reinforcement learning, many different features such as a value function and a policy can be transferred from a source domain to a related target domain. Many researches focused on transfer using hand-coded translation functions that are designed by the experts a priori. However, it is not only very costly but also problem dependent. We propose to apply the Graph Laplacian that is based on the spectral graph theory to decompose the value functions of both a source domain and a target domain into a sum of basis functions respectively. The transfer learning can be carried out by transferring weights on the basis functions of a source do-main to a target domain. We investigate two types of domain transfer, scaling and topological. The results demonstrated that the transferred policy is a better prior policy to reduce the learning time.

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