Reinforcement learning, in the form of Adaptive Critic Designs (ACDs), have the ability to analyse or evaluate a situation and respond to it accordingly. They offer an excellent alternative for adaptively controlling and optimising the highly nonlinear processes found in industry. Here, an enhanced implementation of the action dependent adaptive critic design (ADAC) of Si and Wang [9] is investigated for modelling and control of an industrial grinding process used in the manufacture of hard disk drive platters. This study, one of the first reported industrial applications of this emerging technology, shows that the proposed ADAC control scheme can achieve a 33% reduction in platter rejects compared to an existing proprietary controller.