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Chaos and periodic dynamics in adaptive motion control systems under unknown environment

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2 Author(s)
Sano, M. ; Res. Inst. of Electr. Commun., Tohoku Univ., Sendai, Japan ; Ochini, S.

Predicting and controlling dynamical systems in a previously unknown environment are difficult but challenging problems for adaptive learning and control. We examine a neuron based reinforcement learning algorithm for prediction and control of ball games such as tennis, where dynamical equations, environment, and the behavior of the opponent player are a priori unavailable. We show that stochastic reinforcement learning with a feedforward RBF network is efficient for real time learning and control. Furthermore, reinforcement learning can adapt and control not only periodic motion but also quasi-periodic, and even chaotic orbits of the ball dynamics

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Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on  (Volume:1 )

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