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Reinforcement learning in multi-dimensional state-action space using random rectangular coarse coding and Gibbs sampling

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1 Author(s)
Kimura, H. ; Kyushu Univ., Fukuoka

This paper presents a coarse coding technique and an action selection scheme for reinforcement learning (RL) in multi-dimensional and continuous state-action spaces following conventional and sound RL manners. RL in high-dimensional continuous domains includes two issues: One is a generalization problem for value-function approximation, and the other is a sampling problem for action selection over multi-dimensional continuous action spaces. The proposed method combines random rectangular coarse coding with an action selection scheme using Gibbs-sampling. The random rectangular coarse coding is very simple and quite suited both to approximate Q-functions in high-dimensional spaces and to execute Gibbs sampling. Gibbs sampling enables us to execute action selection following Boltsmann distribution over high-dimensional action space. The algorithm is demonstrated through Rod in maze problem and a redundant-arm reaching task comparing with conventional regular grid approaches.

Published in:

Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on

Date of Conference:

Oct. 29 2007-Nov. 2 2007

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