Input space representation for refinement learning control

TL;DR: An approach to eliminating the quantization of the input space is described and a new input space representation consists of functions that act as receptive fields and have the shape of multivariate Gaussian probability density functions; they are the first layer in the learning network.

Abstract: A learning control approach called refinement, in which a fixed controller is first designed using analytic design tools is explored. This controller's performance is refined by a secondary learning controller, which is a reinforcement learning-based connectionist network. The issue is the representation of the input space of the refinement learning controller. In previous work, the input space was quantized into fixed boxes and each box became a control situation for the learning controller. The drawback was that the learning control designer had to know how to quantize the space. An approach to eliminating the quantization of the input space is described. The new input space representation consists of functions that act as receptive fields and have the shape of multivariate Gaussian probability density functions; they are the first layer in the learning network. Experiments used a tracking control problem with an additive nonlinearity. The learning controller adds an appropriate control signal on the basis of a given evaluation function, in order to improve the fixed controller's ability to track a reference signal. >