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Control of robotic manipulators using a CMAC-based reinforcement learning system

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2 Author(s)
Mei Han ; Dept. of Comput. Sci., Tsinghua Univ., Beijing, China ; Bo Zhang

A practical learning control system is described in this paper, which is applicable to the control of complex robotic systems. In the controller, a stochastic reinforcement learning algorithm is used to learn functions with continuous outputs as control signals. The authors present a CMAC-based network incorporating stochastic real-valued units that learns to perform an underconstrained positioning task using a simulated 2-degree-of-freedom robot arm. The authors also investigate the effects of varying learning algorithm parameters

Published in:

Intelligent Robots and Systems '94. 'Advanced Robotic Systems and the Real World', IROS '94. Proceedings of the IEEE/RSJ/GI International Conference on  (Volume:3 )

Date of Conference:

12-16 Sep 1994