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A new criterion using information gain for action selection strategy in reinforcement learning

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3 Author(s)
Iwata, K. ; Graduate Sch. of Informatics, Kyoto Univ., Japan ; Ikeda, K. ; Sakai, H.

In this paper, we regard the sequence of returns as outputs from a parametric compound source. Utilizing the fact that the coding rate of the source shows the amount of information about the return, we describe ℓ-learning algorithms based on the predictive coding idea for estimating an expected information gain concerning future information and give a convergence proof of the information gain. Using the information gain, we propose the ratio ω of return loss to information gain as a new criterion to be used in probabilistic action-selection strategies. In experimental results, we found that our ω-based strategy performs well compared with the conventional Q-based strategy.

Published in:

Neural Networks, IEEE Transactions on  (Volume:15 ,  Issue: 4 )

Date of Publication:

July 2004

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