  In this paper time-driven learning refers to the machine learning method that
updates parameters in a prediction model continuously as new data arrives.
Among existing approximate dynamic programming (ADP) and reinforcement learning
(RL) algorithms, the direct heuristic dynamic programming (dHDP) has been shown
an effective tool as demonstrated in solving several complex learning control
problems. It continuously updates the control policy and the critic as system
states continuously evolve. It is therefore desirable to prevent the
time-driven dHDP from updating due to insignificant system event such as noise.
Toward this goal, we propose a new event-driven dHDP. By constructing a
Lyapunov function candidate, we prove the uniformly ultimately boundedness
(UUB) of the system states and the weights in the critic and the control policy
networks. Consequently we show the approximate control and cost-to-go function
approaching Bellman optimality within a finite bound. We also illustrate how
the event-driven dHDP algorithm works in comparison to the original time-driven
dHDP.
