Publication Details
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Author(s): A. Fachantidis, I. Partalas, M. Taylor, I. Vlahavas.
Title: “Transfer Learning via Multiple Inter-Task Mappings”.
Availability:
Click here to download the PDF (Acrobat Reader) file (12 pages).
Keywords:
Appeared in:
Recent Advances in Reinforcement Learning, Springer Berlin Heidelberg, pp. 225-236, 2012. 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.
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