This paper describes a real-time beam tuning method with an improved asynchronous advantage actor–critic (A3C) algorithm for accelerator systems. The oper-ating parameters of devices are usually inconsistent with the predictions of physical designs because of errors in mechanical matching and installation. Therefore, parame-ter optimization methods such as pointwise scanning, evolutionary algorithms (EAs), and robust conjugate direction search are widely used in beam tuning to com-pensate for this inconsistency. However, it is difficult for them to deal with a large number of discrete local optima. The A3C algorithm, which has been applied in the auto-mated control field, provides an approach for improving multi-dimensional optimization. The A3C algorithm is introduced and improved for the real-time beam tuning code for accelerators. Experiments in which optimization is achieved by using pointwise scanning, the genetic algo-rithm (one kind of EAs), and the A3C-algorithm are con-ducted and compared to optimize the currents of four steering magnets and two solenoids in the low-energy beam transport section (LEBT) of the Xi’an Proton Application Facility. Optimal currents are determined when the highest transmission of a radio frequency quad-rupole (RFQ) accelerator downstream of the LEBT is achieved. The optimal work points of the tuned accelerator were obtained with currents of 0 A, 0 A, 0 A, and 0.1 A, for the four steering magnets, and 107 A and 96 A for the two solenoids. Furthermore, the highest transmission of the RFQ was 91.2%. Meanwhile, the lower time required for the optimization with the A3C algorithm was successfully verified. Optimization with the A3C algorithm consumed 42% and 78% less time than pointwise scanning with random initialization and pre-trained initialization of weights, respectively.