Reinforcement learning control of robot manipulators in uncertain environments

Considerable attention has been given to the design of stable controllers for robot manipulators, in the presence of uncertainties. We investigate here the robust tracking performance of reinforcement learning control of manipulators, subjected to parameter variations and extraneous disturbances. Robustness properties in terms of average error, absolute maximum errors and absolute maximum control efforts, have been compared for reinforcement learning systems using various parameterized function approximators, such as fuzzy, neural network, decision tree, and support vector machine. Simulation results show the importance of fuzzy Q-learning control. Further improvements in this control approach through dynamic fuzzy Q-learning have also been highlighted.