Browse > Article
http://dx.doi.org/10.9728/dcs.2018.19.1.35

Multi Behavior Learning of Lamp Robot based on Q-learning  

Kwon, Ki-Hyeon (Department of Information & Communication Engineering, Kangwon National University)
Lee, Hyung-Bong (Department of Computer Science & Engineering, Gangneung-Wonju National University)
Publication Information
Journal of Digital Contents Society / v.19, no.1, 2018 , pp. 35-41 More about this Journal
Abstract
The Q-learning algorithm based on reinforcement learning is useful for learning the goal for one behavior at a time, using a combination of discrete states and actions. In order to learn multiple actions, applying a behavior-based architecture and using an appropriate behavior adjustment method can make a robot perform fast and reliable actions. Q-learning is a popular reinforcement learning method, and is used much for robot learning for its characteristics which are simple, convergent and little affected by the training environment (off-policy). In this paper, Q-learning algorithm is applied to a lamp robot to learn multiple behaviors (human recognition, desk object recognition). As the learning rate of Q-learning may affect the performance of the robot at the learning stage of multiple behaviors, we present the optimal multiple behaviors learning model by changing learning rate.
Keywords
Reinforcement Learning; Behavior Coordination; Lamp Robot; Physical Robot; Q-learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 R. Brooks, "A Robust Layered Control System For a Mobile Robot," IEEE Journal of Robotics and Automation, Vol. 2, No. 1, pp. 14-23, 1986.   DOI
2 R. Hafner, and M. Riedmiller, "Reinforcement Learning on a Omnidirectional Mobile Robot," in Proceeding of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, Las Vegas, pp. 418-423, 2003.
3 R.S. Sutton, and A.G. Barto, "Reinforcement Learning, an Introduction," MIT Press, Massachusets, 1998.
4 H. Wicaksono, Prihastono, K. Anam, S. Kuswadi, R. Effendie, A. Jazidie, I. A. Sulistijono, M. Sampei, "Modified Fuzzy Behavior Coordination for Autonomous Mobile Robot Navigation System," in Proceeding of ICCAS-SICE, 2009.
5 C. Watkins and P. Dayan, "Q-learning, Technical Note," Machine Learning, Vol 8, pp. 279-292, 1992.
6 Y. G. Seo, "LoRa Network based Parking Dispatching System : Queuing Theory and Q-learning Approach," The Journal of Digital Contents Society, Vol. 18, No. 7, pp. 1443-1450, June 2017.   DOI
7 K. Anam, S. Kuswadi, "Behavior Based Control and Fuzzy Q-Learning For Autonomous Mobile Robot Navigation," in Proceeding of The 4th International Conference on Information & Communication Technology and Systems (ICTS), 2008.
8 S. M. Rho, "LoRa Network based Parking Dispatching System : Queuing Theory and Q-learning Approach," The Journal of Digital Contents Society, Vol. 18, No. 7, pp. 1443-1450, June 2017.   DOI
9 M.C. Perez, A Proposal of Behavior Based Control Architecture with Reinforcement Learning for an Autonomous Underwater Robot, Ph.D. Dissertation, University of Girona, Girona, 2003.
10 L. Khriji, F. Touati, K. Benhmed, A.A. Yahmedi, "Q-Learning Based Mobile robot behaviors Coordination," in Proceeding of International Renewable Energy Congress (IREC), 2010.
11 J.L LIN, K.S. HWANG, W.C. JIANG, and Y.J. CHEN, "Gait Balance and Acceleration of a Biped Robot Based on Q-Learning," IEEE Access, Vol. 4, pp. 2439-2449, 2016.   DOI
12 C. J. C. H.Watkins, Learning from delayed rewards, Ph.D. dissertation, Dept. Psychol., Univ. Cambridge, Cambridge, U.K., 1989.
13 H.Wicaksono, "Q Learning Behavior on Autonomous Navigation of Physical Robot," The 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI 2011), in Songdo Convention, Incheon, Korea, Nov. 23-26, 2011.
14 C. F. Touzet, "Q-learning for robot,'' in The Handbook of Brain Theory and Neural Networks, M. A. Arbib, Ed. Cambridge, MA, USA: MIT Press, pp. 934-937, 2003.