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We investigate the applications of a class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming (ADHDP). Adaptive critic designs are defined as designs that approximate dynamic programming in the general case, i.e., approximate optimal control over time in noisy, nonlinear environment. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data obtained from a test vehicle, we first develop a neural network model of the engine. The neural network controller is then designed based on the idea of approximate dynamic programming to achieve optimal control. In the simulation studies, the initial controller is trained using the neural network engine model developed rather than the actual engine. We have developed and tested self-learning neural network controllers for both engine torque and exhaust air-fuel ratio control. The goal of the engine torque control is to track the commanded torque. The objective of the air-fuel ratio control is to regulate the engine air-fuel ratio at specified set points. For both control problems, good transient performance of the neural network controller has been observed. A distinct feature of the present technique is the controller's real-time adaptation capability which allows the neural network controller to be further refined and improved in real-time vehicle operation through continuous learning and adaptation.