Reinforcement Learning Control Schemes

−In this paper, the use of Artificial Neural Network for the control of non-linear plants is explored. As the plant parameters or model is considered unknown, it is necessary to use plant input/output to train the controller and therefore reinforcement learning control schemes are devised to achieve desired results. The main features of the developed schemes are that few trials are required to train the controllers and a variety of control actions is taken rather than only two actions as in the standard reinforcement schemes. In addition, a supervised neural network controller, which is trained using the reinforcement control schemes, is proposed. An example of a magnetic suspension system is presented to illustrate the effectiveness of the control algorithms given. For comparison purposes, results of a linear optimal controller are included.