Hexagon-Based Q-Learning Algorithm and Applications

  • Yang, Hyun-Chang (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Kim, Ho-Duck (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Yoon, Han-Ul (School of Electrical and Electronics Engineering, the University of Illinois at Urbana Champaign) ;
  • Jang, In-Hun (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
  • Published : 2007.10.31

Abstract

This paper presents a hexagon-based Q-leaning algorithm to find a hidden targer object with multiple robots. An experimental environment was designed with five small mobile robots, obstacles, and a target object. Robots went in search of a target object while navigating in a hallway where obstacles were strategically placed. This experiment employed two control algorithms: an area-based action making (ABAM) process to determine the next action of the robots and hexagon-based Q-learning to enhance the area-based action making process.

Keywords

References

  1. L. Parker, 'Adaptive action selection for cooperative agent teams,' Proc. of the 2nd International Conference on Simulation of Adaptive Behavior, pp. 15-64, 1992
  2. G. Ogasawara, T. Omata, and T. Sato, 'Multiple movers using distributed, decision-theoretic control,' Proc. of Japan-USA Symposium on Flexible Automation, vol. 1, pp. 623-630, 1992
  3. D. Ballard, An Introduction to Natural Computation, The MIT Press, Cambridge, 1997
  4. J. Jang, C. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice-Hall, New Jersey, 1997
  5. W. Ashley and T. Balch, 'Value-based observation with robot teams (VBORT) using probabilistic techniques,' Proc. of International Conference on Advanced Robotics, 2003
  6. J. B. Park, B. H. Lee, and M. S. Kim, 'Remote control of a mobile robot using distance-based reflective force,' Proc. of IEEE International Conference on Robotics and Automation, vol. 3, pp. 3415-3420, 2003
  7. D. Patterson and J. Hennessy, Computer Organization and Design, 3rd ed., Morgan-Kaufmann, Korea, 2005
  8. T. Mitchell, Machine Learning, McGraw-Hill, Singapore, 1997
  9. C. Clausen and H. Wechsler, 'Quad Q-learning,' IEEE Trans. on Neural Network, vol. 11, pp. 279-294, 2000 https://doi.org/10.1109/72.839000
  10. S. Russel and P. Norbig, Artificial Intelligence: A Modern Approach, 2nd ed., Prentice-Hall, New Jersey, 2003
  11. H. U. Yoon and K. B. Sim, 'Hexagon-based Qlearning for object search with multiple robots,' Lecture Notes in Computer Science (LNCS), Springer-Verlag, vol. 3612, pp. 713-722, 2005 https://doi.org/10.1007/11539902_88
  12. H. U. Yoon and K. B. Sim, 'Hexagon-based Qlearning to find a hidden target object,' Lecture Notes in Artificial Intelligence (LNAI), Springer-Verlag, vol. 3801, pp. 429-434, 2005
  13. S. H. Whang, K. B. Sim, I. C. Jeong, et al., 'Design of efficient strategies for distributed multi-agent robot soccer system,' Proc. of the FIRA Robot Congress, 2004
  14. Bluetooth Co., Specification of the Bluetooth System, vol. 1, pp. 537-828, 2001
  15. H. U. Yoon, S. H. Hwang, D. W. Kim, D. H. Lee, and K. B. Sim, 'Robotic agent design and application in the ubiquitous intelligent space,' Journal of Control, Automation, and Systems Engineering (Korean), vol. 11, no. 12, pp. 1039-1044, 2005 https://doi.org/10.5302/J.ICROS.2005.11.12.1039