Fusion of multiple behaviors using layered reinforcement learning

IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans

Volumn 42, Issue 4, 2012, Pages 999-1004

  Hwang, Kao Shing ^a   Chen, Yu Jen ^b   Wu, Chun Ju ^c  

^a NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

^b NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

^c INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE   (Taiwan)

Author keywords

Behavior based control; intelligent robots; reinforcement learning

Indexed keywords

BEHAVIOR-BASED CONTROL; COMPLEX BEHAVIOR; CONTROL POLICY; DYNAMIC ENVIRONMENTS; POST PROCESS; Q-LEARNING; Q-LEARNING ALGORITHMS; STATE SPACE; TREE INDUCTION;

DECISION TREES; INTELLIGENT ROBOTS; LEARNING ALGORITHMS; REINFORCEMENT LEARNING;

BEHAVIORAL RESEARCH;

EID: 84862798631     PISSN: 10834427     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCA.2012.2183349     Document Type: Article

References (18)

1. D. Dong, C. Chen, H. Li, and T. J. Tarn, "Quantum reinforcement learning," IEEE Trans. Syst.,Man, Cybern. B, Cybern., vol. 38, no. 5, pp. 1207-1220, Oct. 2008.
2. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press, 1998.
3. P. He and S. Jagannathan, "Reinforcement learning-based output feedback control of nonlinear systems with input constraints," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 35, no. 1, pp. 150-154, Feb. 2005.
4. S. R. Safavian and D. Landgrebe, "A survey of decision tree classifier methodology," IEEE Trans. Syst., Man, Cybern., vol. 21, no. 3, pp. 660-674, May/Jun. 1991.
5. S. K. Murthy, "Automatic construction of decision trees from data: A multi-disciplinary survey," Data Mining Knowl. Disc., vol. 2, no. 4, pp. 345-389, Dec. 1998.
6. R. Brooks, "A robust layered control system for a mobile robot," IEEE J. Robot. Autom., vol. RA-2, no. 1, pp. 14-23, Mar. 1986.
7. Y. Takahashi, K. Noma, and M. Asada, "Efficient behavior learning based on state value estimation of self and others," Adv. Robot., vol. 22, no. 12, pp. 1379-1395, 2008.
8. A. Kleiner, M. Dietl, and B. Nebel, "Towards a life-long learning soccer agent," in Proc. Int. RoboCup Symp., Jun. 2002, pp. 119-127.
9. H. Hattori, Y. Nakajima, and T. Ishida, "Learning from humans: Agent modeling with individual human behaviors," IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 41, no. 1, pp. 1-9, Jan. 2011.
10. J. Simpson, C. L. Jacobsen, and M. C. Jadud, "Mobile robot control: The subsumption method and Occam-Pi," in Proc. Commun. Process Arch., 2006, pp. 225-236.
11. L. Busoniu, R. Babuska, and B. Schutter, "A comprehensive survey of multiagent reinforcement learning," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 2, pp. 156-172, Mar. 2008.
12. L. Hyafil and R. L. Rivest, "Constructing optimal binary decision trees is NP-complete," Inf. Process. Lett., vol. 5, no. 1, pp. 15-17, 1976.
13. W. Tong, H. Hong, H. Fang, Q. Xie, and R. Perkins, "Decision forest: Combining the predictions of multiple independent decision tree models," J. Chem. Inf. Comput. Sci, vol. 43, no. 2, pp. 525-531, Mar./Apr. 2003.
14. S. K. Murthy and S. Salzberg, "Lookahead and pathology in decision tree induction," in Proc. 17th Int. Joint Conf. Artif. Intell., San Mateo, CA, 1995, pp. 1025-1031.
15. O. Simsek and A. G. Barto, "Using relative novelty to identify useful temporal abstractions in reinforcement learning," in Proc. 21st Int. Conf. Mach. Learn., 2004, p. 95.
16. K. S. Hwang, Y. J. Chen, and T. H. Yang, "Behavior cloning by a selforganizing decision tree," in Proc. IEEE ICIT, 2007, pp. 20-24.
17. K. S. Hwang, T. W. Yang, and C. J. Lin, "Self organizing decision tree based on reinforcement learning and its application on state space partition," in Proc. IEEE Int. Conf. Syst., Man, Cybern., 2006, pp. 5088-5093.
18. M. Dong and R. Kothari, "Look-ahead based fuzzy decision tree induction," IEEE Trans. Fuzzy Syst., vol. 9, no. 3, pp. 461-468, Jun. 2001.