||Author(s): A. Fachantidis, I. Partalas, M. Taylor, I. Vlahavas.
Title: “Transfer Learning via Multiple Inter-Task Mappings”.
Click here to download the PDF (Acrobat Reader) file (12 pages).
Accepted for presentation at the 9th European Workshop on Reinforcement Learning and to be published in the Workshop Proceedings, 2011.
Abstract: In this paper we investigate using multiple mappings for
transfer learning in reinforcement learning tasks. We propose two dif-
ferent transfer learning algorithms that are able to manipulate multiple
inter-task mappings for both model-learning and model-free reinforce-
ment learning algorithms. Both algorithms incorporate mechanisms to
select the appropriate mappings, helping to avoid the phenomenon of
negative transfer. The proposed algorithms are evaluated in the Moun-
tain Car and Keepaway domains. Experimental results show that the use
of multiple inter-task mappings can signiﬁcantly boost the performance
of transfer learning methodologies, relative to using a single mapping or
learning without transfer.