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An adaptive clustering method for model-free reinforcement learning

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2 Author(s)
Matt, A. ; Inst. of Math., Innsbruck Univ., Austria ; Regensburger, G.

Machine learning for real world applications is a complex task due to the huge state and action sets they deal with and the a priori unknown dynamics of the environment involved. Reinforcement learning offers very efficient model-free methods which are often combined with approximation architectures to overcome these problems. We present a Q-learning implementation that uses a new adaptive clustering method to approximate state and actions sets. Experimental results for an obstacle avoidance behavior with the mobile robot Khepera are given.

Published in:

Multitopic Conference, 2004. Proceedings of INMIC 2004. 8th International

Date of Conference:

24-26 Dec. 2004

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