In this paper, we research the resilient control problem for cyber-physical system (CPS) under denial-of-service (DoS) attacks. These malicious DoS attacks aim to impede the communication of measurement data or control data in order to endanger the functionality of the closed-loop system. Meanwhile, in order to save network resources, event-triggered mechanism has been introduced into this CPS. By exploiting the relationship between cyber system and physical system, we aim to design the resilient controller and resilient control strategy to tolerate a class of DoS signals characterised by probability without serious hazard to the stability and performance of CPS. Furthermore, considering that the transition probability of cyber state is unknown, the on-policy reinforcement learning method - SARSA (State-Action-Reward-State-Action) - is used to solve this problem. Thus a resilient control algorithm that integrates game theory, robust control theory, event-triggered control method and SARSA learning method is presented to enhance the security and robustness of the CPS. At last, the numerical simulation and experimental results are given to demonstrate the validity and applicability of the proposed algorithm.