Research on Control of Move-in-mud Robot Based on Q Learning

DOI

10.2991/icence-16.2016.177How to use a DOI?

Keywords

move-in-mud robot; motion control; Q learning; radial basis function; neural network

Abstract

In order to improve the behavior self-control ability of move-in-mud robot in unknown environment, this paper proposes a behavior control algorithm based on radial basis function neural network Q learning. The algorithm enhances the interaction between robot and environment and improves self-learning ability through using the enhanced Q learning method. By employing the radial basis function neural network to approximate the state space and Q function, the learning system has good generalization ability and effectively solves the dimension disaster problem of the state space under complex and continuous environment. Simulation experiment results show that this method not only can make move-in-mud robot have strong motion control ability, but also improve the ability of robot to adapt to the environment.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yunming Du
AU  - Bingbing Yan
AU  - Yongcheng Jiang
PY  - 2016/09
DA  - 2016/09
TI  - Research on Control of Move-in-mud Robot Based on Q Learning
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 949
EP  - 954
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.177
DO  - 10.2991/icence-16.2016.177
ID  - Du2016/09
ER  -