A Policy-Improving System for Adaptability to Dynamic Environments Using Mixture Probability and Clustering Distribution

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ISSN Print: 2327-5219
ISSN Online: 2327-5227
www.scirp.org/journal/jcc
E-mail: jcc@scirp.org

"A Policy-Improving System for Adaptability to Dynamic Environments Using Mixture Probability and Clustering Distribution"
written by Uthai Phommasak, Daisuke Kitakoshi, Jun Mao, Hiroyuki Shioya,
published by Journal of Computer and Communications, Vol.2 No.4, 2014

has been cited by the following article(s):

[1]	An empirical study on evaluating basic characteristics and adaptability to users of a preventive care system with learning communication robots
	Soft Computing, 2015

[2]	A Novel exploration/exploitation policy accelerating learning in both stationary and non-stationary environment navigation tasks
	International Journal of Computer and Electrical Engineering, 2015

[3]	動的な階層環境における強化学習エージェントの確率知識を用いた方策改善に関する研究
	2015

[4]	A Novel Exploration/Exploitation Policy Accelerating Environment Navigation Tasks
	2015

[5]	A Reinforcement Learning System to Dynamic Movement and Multi-Layer Environments
	Journal of Intelligent Learning Systems and Applications, 2014

[1]	An empirical study on evaluating basic characteristics and adaptability to users of a preventive care system with learning communication robots Soft Computing, 2017 DOI:10.1007/s00500-015-1631-7

[2]	A Novel Exploration/Exploitation Policy Accelerating Learning in Both Stationary and Non-Stationary Environment Navigation Tasks International Journal of Computer and Electrical Engineering, 2015 DOI:10.17706/IJCEE.2015.7.3.149-158

[3]	A Reinforcement Learning System to Dynamic Movement and Multi-Layer Environments Journal of Intelligent Learning Systems and Applications, 2014 DOI:10.4236/jilsa.2014.64014

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