Event-triggered reinforcement learning approach for unknown nonlinear continuous-time system
Date of Original Version
This paper provides an adaptive event-triggered method using adaptive dynamic programming (ADP) for the nonlinear continuous-time system. Comparing to the traditional method with fixed sampling period, the event-triggered method samples the state only when an event is triggered and therefore the computational cost is reduced. We demonstrate the theoretical analysis on the stability of the event-triggered method, and integrate it with the ADP approach. The system dynamics are assumed unknown. The corresponding ADP algorithm is given and the neural network techniques are applied to implement this method. The simulation results verify the theoretical analysis and justify the efficiency of the proposed event-triggered technique using the ADP approach.
Publication Title, e.g., Journal
Proceedings of the International Joint Conference on Neural Networks
Zhong, Xiangnan, Zhen Ni, Haibo He, Xin Xu, and Dongbin Zhao. "Event-triggered reinforcement learning approach for unknown nonlinear continuous-time system." Proceedings of the International Joint Conference on Neural Networks (2014): 3677-3684. doi: 10.1109/IJCNN.2014.6889787.