Reinforcement learning control of a real mobile robot using approximate policy iteration

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5553 LNCS, Issue PART 3, 2009, Pages 278-288

  Zhang, Pengcheng ^a   Xu, Xin ^a   Liu, Chunming ^a   Yuan, Qiping ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Approximate dynamic programming; Approximate policy iteration; Mobile robots; Path following; Reinforcement learning

Indexed keywords

APPROXIMATE DYNAMIC PROGRAMMING; APPROXIMATE POLICY ITERATION; APRIORI; CONTINUOUS SPACES; CONTROL METHODS; CONTROL STRATEGIES; EXPERIMENTAL STUDIES; KERNEL MACHINE; LEARNING CONTROL; LEARNING CONTROLLERS; LEARNING EFFICIENCY; LEARNING TECHNIQUES; LINEAR APPROXIMATIONS; MACHINE-LEARNING; ON DYNAMICS; OPTIMIZED CONTROL; PATH FOLLOWING; PATH FOLLOWING CONTROL; PD CONTROL; POLICY ITERATION; REINFORCEMENT LEARNING CONTROL;

APPLICATION PROGRAMMING INTERFACES (API); DYNAMIC PROGRAMMING; EDUCATION; LEARNING ALGORITHMS; MOBILE ROBOTS; NEURAL NETWORKS; REINFORCEMENT; REINFORCEMENT LEARNING; ROBOT PROGRAMMING; SYSTEMS ENGINEERING;

WIRELESS NETWORKS;

EID: 69849102820     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01513-7_30     Document Type: Conference Paper

References (17)

1. Campion, G.: Structural Properties and Classification of Dynamic Models of Wheeled Mobile Robots. IEEE Trans. on Robotics and Automation 12, 47-62 (1996)
2. Alexander, J.C., Brooks, J.H.: On the Kinematics of Wheeled Mobile Robots. Int. J. of Robotics Research 8, 15-27 (1989)
3. Chiacchio, P.: Exploiting Redundancy in Minimum-time Path Following Robot Control. In: American Control Conference (1982)
4. Sarkar, N., Gen, V.: Dynamic Path Following: A New Control Algorithm for Mobile Robots. In: 32nd Conference on Decision and Control, pp. 2670-2675. IEEE Press, New York (1993)
5. Coelho, P., Numes, U.: Path Following Control of Mobile Robots in Presence of Uncertainties. IEEE Transaction on Robotics 21, 252-261 (2005)
6. Brooks, R.: A Hardware Retargetable Distributed Layered Architecture for Mobile Robot Control. In: IEEE International Conference on Robotics and Automation, pp. 106-110. IEEE Press, New York (1987)
7. Chen, C.L., Chen, C.H.: Reinforcement Learning for Mobile Robot from Reaction to Deliberation. Journal of Systems Engineering and Electronic 16, 611-617 (2005)
8. Sutton, R., Barto, A.: Reinforcement Learning, an Introduction. MIT Press, Cambridge (1998)
9. Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement Learning: A Survey. J. Artif. Intell. Res. 4, 237-285 (1996)
10. Smart, W.D., Kaelbing, L.P.: Effective Reinforcement Learning for Mobile Robots. In: IEEE International Conference on Robotics and Automation, pp. 3404-3410. IEEE Press, New York (2002)
11. Xu, X., Hu, D.W., Lu, X.C.: Kernel-Based Least Squares Policy Iteration for Reinforcement Learning. IEEE Transaction on Neural Networks 18, 973-992 (2007)
12. Canudas, C., Sordalen, O.J.: Exponential Stabilization of Mobile Robots with Non-holonomic Constraints. IEEE Transactions on Automatic Control 33, 672-677 (1992)
13. Boyan, J.: Technical Update: Least-squares Temporal Difference Learning. Mach. Learn. 49, 233-246 (2002)
14. Lagoudakis, M.G., Parr, R.: Least-squares Policy Iteration. J. Mach. Learn. Res. 4, 1107-1149 (2003)
15. Engel, Y., Mannor, S., Meir, R.: The Kernel Recursive Least-squares Algorithm. IEEE Trans. Signal Process. 52, 2275-2285 (2004)
16. Bertsekas, D.P., Tsitsiklis, J.N.: Neuro-Dynamic Programming. Athena Scientific, Belmont (1996)
17. Lagoudakis, M.G., Parr, R.: Least-squares Policy Iteration. J. Mach. Learn. Res. 4, 1107-1149 (2003)