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Appeared in: Recent Advances in Reinforcement Learning, Lecture Notes in Computer Science, Springer-Verlag, 7188, pp. 213-224, 2012.

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.

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