 This paper proposes an event-triggered learning control algorithm for hydraulic servo actuator systems. The algorithm uses adaptive dynamic programming to determine optimal control actions without requiring knowledge of the system's dynamics or parameters. Additionally, it utilizes an event-based feedback strategy to reduce the amount of communication required between the controller and the system. The algorithm was tested using simulation results and demonstrated its ability to achieve optimal performance while reducing the amount of communication required. This article was authored by Vladimir Georgievich, Hongfeng Tao, Xiaona Song, and others.