Consistency of fuzzy model-based reinforcement learning

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2008, Pages 518-524

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

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^c University of Liège ^*

^d UNIVERSITY OF LIÈGE   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY LOGIC; FUZZY SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; POLYNOMIAL APPROXIMATION; PROBABILITY DENSITY FUNCTION; REINFORCEMENT; REINFORCEMENT LEARNING; SOLUTIONS;

ACTION SPACES; APPROXIMATE ALGORITHMS; APPROXIMATION ACCURACIES; CONTROL ACTIONS; DISCRETE SETS; DISCRETIZATION; EXPERIMENTAL STUDIES; FUZZY MODELS; FUZZY PARTITIONS; ITERATION ALGORITHMS; LEARNING CONTROLS; MODEL-BASED; OPTIMAL SOLUTIONS; PROCESS STATES; REWARD FUNCTIONS; STATE SPACES;

APPROXIMATION ALGORITHMS;

EID: 55249099118     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2008.4630417     Document Type: Conference Paper

References (23)

1. D. P. Bertsekas, Dynamic Programming and Optimal Control. 3rd ed. Athena Scientific, 2007, vol. 2.
2. R. S. Sutton and A. G. Barlo, Reinforcement Learning: An Introduction. MIT Press, 1998.
3. L. Buşoniu, D. Ernst, B. De Schutter. and R. Babuska, "Fuzzy approximation for convergent model-based reinforcement learning," in Proceedings 2007 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE-07). London, UK, 23-26 July 2007. pp. 968-973.
4. _, "Continuous-state reinforcement learning with fuzzy approximation," in Adaptive Agents and Multi-Agent Systems III, ser. Lecture Notes in Computer Science, K. Tuyls. A. Nowé, Z. Guessoum, and D. Kudenko. Eds. Springer. 2008, vol. 4865, pp. 27-43.
5. P. Y. Glorennee, "Reinforcement learning: An overview," in Proceedings European Symposium on Intelligent Techniques (ESIT-00). Aachen, Germany. 14-15 September 2000. pp. 17-35.
6. T. Horiuchi, A. Fujinci, O. Katai, and T. Sawaragi, "Fuzzy interpolation-based Q-learning 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.
7. 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.
8. 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.
9. 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.
10. 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.
11. C.-K. Lin. "A reinforcement learning adaptive fuzzy controller for robots," Fuzzy Sets and Systems, vol. 137, no. 3, pp. 339-352, 2003.
12. [ 12] C.-S. Chow and J. Tsitsiklis, "An optimal one-way multigrid algorithm for discrete-time stochastic control," IEEE Transactions on Automatic Control, vol. 36, no. 8, pp. 898-914, 1991.
13. 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.
14. 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.
15. M. S. Santos and J. Vigo-Aguiar, "Analysis of a numerical dynamic programming algorithm applied to economic models." Econometrica, vol. 66, no. 2, pp. 409-426, 1998.
16. C. Szepesvári and R. Munos, "Finite time bounds for sampling based fitted value iteration," in Proceedings Twenty-Second International Conference on Machine Learning (ICML-05). Bonn, Germany, 7-11 August 2005, pp. 880-887.
17. M. Wiering, "Convergence and divergence in standard and averaging reinforcement learning," in Proceedings 15th European Conference on Machine Learning (ECML'04), Pisa. Italy, 20-24 September 2004, pp. 477-488.
18. D. Ormoneit and S. Sen. "Kernel-based reinforcement learning," Machine Learning, vol. 49, no. 2-3, pp. 161-178, 2002.
19. D. Ernst, P. Geurts, and L. Wehenkel, "Tree-based batch mode reinforcement learning," Journal of Machine Learning Research, vol. 6, pp. 503-556, 2005.
20. C. Szepesvári and W. D. Smart, "Interpolation-based Q-learning," in Proceedings Twenty-First International Conference on Machine Learning (ICML-04), Bannf, Canada, 4-8 July 2004, pp. 791-798.
21. S. P. Singh. T. Jaakkola, and M. I. Jordan, "Reinforcement learning with soft state aggregation." in Advances in Neural Information Processing Systems 7, G. Tesauro, D. S. Touretzky, and T. K. Leen, Eds., 1995, pp. 361-368.
22. D. Ernst. "Near optimal closed-loop control. Application to electric power systems," Ph.D. dissertation, University of Liège, Belgium. March 2003.
23. A. Y. Ng, D. Harada, and S. Russell, "Policy invariance under reward transformations: Theory and application to reward shaping," in Proceedings Sixteenth International Conference on Machine Learning (ICML'99), Bled, Slovenia, 27-30 June 1999, pp. 278-287.