This paper presents a novel sequential learning neural network implementation of action dependent adaptive critics. Sequential learning neural networks provide a systematic way of adding neurons in response to new data features as well as removing neurons which cease to contribute to the overall performance of the network. The convergence rate of the sequential learning method is enhanced by applying a modified Recursive Prediction Error algorithm to adjust network parameters. The new methodology, which provides a fully autonomous controller, is benchmarked against the conventional MLP neurocontroller on a highly nonlinear inverted pendulum system and shown to achieve superior performance.