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| Title: |
Transferring Evolved Reservoir Features in Reinforcement Learning Tasks |
| Author(s): |
K. Chatzidimitriou, I. Partalas, P. Mitkas, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (12 pages).
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| Keywords: |
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| Appeared in: |
Recent Advances in Reinforcement Learning, Lecture Notes in Computer Science, Springer-Verlag, 7188, pp. 213-224, 2012.
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| Abstract: |
The major goal of transfer learning is to transfer knowledge
acquired on a source task in order to facilitate learning on another, dif-
ferent, but usually related, target task. In this paper, we are using neu-
roevolution to evolve echo state networks on the source task and transfer
the best performing reservoirs to be used as initial population on the tar-
get task. The idea is that any non-linear, temporal features, represented
by the neurons of the reservoir and evolved on the source task, along with
reservoir properties, will be a good starting point for a stochastic search
on the target task. In a step towards full autonomy and by taking advan-
tage of the random and fully connected nature of echo state networks,
we examine a transfer method that renders any inter-task mappings of
states and actions unnecessary.We tested our approach and that of inter-
task mappings in two RL testbeds: the mountain car and the server job
scheduling domains. Under various setups the results we obtained in both
cases are promising. |
| See also : |
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