  This paper investigates the design of self-triggered controllers for
networked control systems (NCSs), where the dynamics of the plant is
\textit{unknown} apriori. To deal with the unknown transition dynamics, we
employ the Gaussian process (GP) regression in order to learn the dynamics of
the plant. To design the self-triggered controller, we formulate an optimal
control problem, such that the optimal control and communication policies can
be jointly designed based on the GP model of the plant. Moreover, we provide an
overall implementation algorithm that jointly learns the dynamics of the plant
and the self-triggered controller based on a reinforcement learning framework.
Finally, a numerical simulation illustrates the effectiveness of the proposed
approach.
