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

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