Fuzzy approximation for convergent model-based reinforcement learning

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2007, Pages

  Buşoniu, Lucian ^a   Ernst, Damien ^b   De Schutter, Bart ^a   Babuška, Robert ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b SUPELEC Campus de Rennes   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION ALGORITHMS; CONTROL THEORY; EDUCATION; FUZZY CONTROL; FUZZY LOGIC; FUZZY SYSTEMS; HEURISTIC PROGRAMMING; ITERATIVE METHODS; LEARNING SYSTEMS; POLYNOMIAL APPROXIMATION; REINFORCEMENT; REINFORCEMENT LEARNING;

APPROXIMATE SOLUTIONS; CONSISTENCY PROPERTIES; CONTROL ACTIONS; CONVERGENCE RESULTS; DISCRETE VALUES; FUZZY APPROXIMATIONS; FUZZY REPRESENTATIONS; HEURISTIC SOLUTIONS; INTERNATIONAL CONFERENCES; LEARNING CONTROL; MODEL-BASED; ORIGINAL ALGORITHMS; Q VALUES; SIMULATION EXAMPLE;

LEARNING ALGORITHMS;

EID: 50249166041     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2007.4295497     Document Type: Conference Paper

References (24)

1. R. S. Sutton and A. G. B arto, Reinforcement Learning: An Introduction. Cambridge, US: MIT Press, 1998.
2. L. P. Kaelbling, M. L. Liftman, and A. W. Moore, "Reinforcement learning: A survey," Journal of Artificial Intelligence Research, vol. 4, pp. 237-285, 1996.
3. D. P. Bertsekas, Dynamic Programming and Optimal Control, 2nd ed. Athena Scientific, 2001, vol. 2.
4. J. N. Tsitsiklis and B. Van Roy, "Feature-based methods for large scale dynamic programming," Machine Learning, vol. 22, no. 1-3, pp. 59-94, 1996.
5. G. Gordon, "Stable function approximation in dynamic programming," in Proceedings Twelfth International Conference on Machine Learning (ICML-95), Tahoe City, US, 9-12 July 1995, pp. 261-268.
6. R. Munos and A. Moore, "Variable-resolution discretization in optimal control," Machine Learning, vol. 1, pp. 1-31, 2001.
7. D. Ormoneit and S. Sen, "Kernel-based reinforcement learning," Machine Learning, vol. 49, pp. 161-178, 2002.
8. D. Ernst, P. Geurts, and L. Wehenkel, "Tree-based batch mode reinforcement learning," Journal of Machine Learning Research, vol. 6, pp. 503-556, 2005.
9. M. G. Lagoudakis and R. Parr, "Least-squares policy iteration," Journal of Machine Learning Research, vol. 4, pp. 1107-1149, 2003.
10. C. Szepesvári and W. D. Smart, "Interpolation-based Q-learning," in Proceedings 21st International Conference on Machine Learning (ICML-04), Bannf, Canada, July 4-8 2004.
11. L. Baird and A. Moore, "Gradient descent for general reinforcement learning," in Advances in Neural Information Processing Systems 11 (NLPS-98), Denver, US, 30 November - 5 December 1998, pp. 968-974.
12. S. P. Singh, T. Jaakkola, and M. I. Jordan, "Reinforcement learning with soft state aggregation," in Advances in Neural Information Processing Systems 7, Denver, Colorado, USA, 1994, pp. 361-368.
13. D. Ernst, "Near optimal closed-loop control. Application to electric power systems," Ph.D. dissertation, University of Liège, Belgium, March 2003.
14. J. Boyan and A. Moore, "Generalization in reinforcement learning: Safely approximating the value function," in Advances in Neural Information Processing Systems 7 (NIPS-94), Denver, Colorado, US, 1994, pp. 369-376.
15. C. J. C. H. Watkins and P. Dayan, "Q-leaming," Macnine Learning, vol. 8, pp. 279-292, 1992.
16. P. Y. Glorennec, "Reinforcement learning: An overview," in Proceedings European Symposium on Intelligent Techniques (ESIT-00), Aachen, Germany, 14-15 September 2000, pp. 17-35.
17. T. Horiuchi, A. Fujino, O. Katai, and T. Sawaragi, "Fuzzy interpolation-based Q-Ieaming with continuous states and actions," in Proceedings 5th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE-96), New Orleans, US, 8-11 September 1996, pp. 594-600.
18. L. Jouffe, "Fuzzy inference system learning by reinforcement methods," IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, vol. 28, no. 3, pp. 338-355, 1998.
19. H. R. Berenji and P. Khedkar, "Learning and tuning fuzzy logic controllers through reinforcements," IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 724-740, 1992.
20. H. R. Berenji and D. Vengerov, "A convergent actor-critic-based FRL algorithm with application to power management of wireless transmitters," IEEE Transactions on Fuzzy Systems, vol. 11, no. 4, pp. 478-485, 2003.
21. D. Vengerov, N. Bambos, and H. R. Berenji, "A fuzzy reinforcement learning approach to power control in wireless transmitters," IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 35, no. 4, pp. 768-778, 2005.
22. A. Bonarini, "Evolutionary learning, reinforcement learning, and fuzzy mies for knowledge acquisition in agent-based systems," Proceedings of the IEEE, vol. 89, no. 9. pp. 1334-1346, 2001.
23. C.-K. Lin, "A reinforcement learning adaptive fuzzy controller for robots," Fuzzy Sets and Systems, vol. 137, pp. 339-352, 2003.
24. S. Jodogne, "Closed-loop learning of visual control policies," Ph.D. dissertation, University of Liège, Belgium, December 2006.