Q-Learning in Continuous State-Action Space with Noisy and Redundant Inputs by Using a Selective Desensitization Neural Network
Takaaki Kobayashi, Takeshi Shibuya, and Masahiko Morita
Faculty of Engineering, Information and Systems, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573 Japan
-  R. S. Sutton and A. G. Barto, “Reinforcement Learning: An Introduction,” MIT Press, 1998.
-  C. J. C. H. Watkins, “Learning from delayed rewards,” Ph.D. thesis, University of Cambridge, 1989.
-  C. J. C. H. Watkins and P. Dayan, “Q-learning,” Machine Learning, Vol.8, pp. 279-292, 1992.
-  C. Gaskett, D. Wettergreen, and A. Zelinsky, “Q-learning in continuous state and action spaces,” Proc. 12th Australian Joint Conf. on Artificial Intell., Sydney, pp. 417-428, 1999.
-  G. Konidaris, S. Osentoski, and P. Thomas, “Value function approximation in reinforcement learning using the Fourier basis,” Proc. 25th Conf. on Artificial Intell., San Francisco, pp. 380-385, 2011.
-  A. Geramifard, M. Bowling, and R. S. Sutton, “Incremental least-square temporal difference learning,” Proc. 21th Conf. on Artificial Intell., Boston, pp. 356-361, 2006.
-  J. Park and I. W. Sandberg, “Universal approximation using radial-basis-function networks,” Neural Computation, Vol.3, No.2, pp. 246-257, 1991.
-  L. Jouffe, “Fuzzy inference system learning by reinforcement methods,” IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol.28, No.3, pp. 338-355, 1998.
-  P. Y. Glorennec, “Reinforcement learning: An overview,” Proc. European Symposium on Intelligent Techniques (ESIT-00), Aachen, pp. 14-35, 2000.
-  K. Nonaka, F. Tanaka, and M. Morita, “Empirical comparison of feedforward neural networks on two-variable function approximation,” IEICE Trans. Inf. & Syst.(Japanese Edition), Vol.94, No.12, pp. 2114-2125, 2011.
-  K. Horie, A. Suemitsu, and M. Morita, “Direct estimation of hand motion speed from surface electromyograms using a selective desensitization neural network,” J. Signal Process., Vol.18, No.4, pp. 225-228, 2014.
-  T. Kobayashi, T. Shibuya, and M. Morita, “Q-learning in Continuous State-Action Space with Redundant Dimensions by Using a Selective Desensitization Neural Network,” Proc. SCIS&ISIS 2014, Kitakyushu, pp. 801-806, 2014.
-  M.W. Spong, “The swing up control problem for the acrobot,” IEEE Control Syst. Mag., Vol.15, No.1, pp. 49-55, 1995.
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