  This paper delves into designing stabilizing feedback control gains for
continuous linear systems with unknown state matrix, in which the control is
subject to a general structural constraint. We bring forth the ideas from
reinforcement learning (RL) in conjunction with sufficient stability and
performance guarantees in order to design these structured gains using the
trajectory measurements of states and controls. We first formulate a
model-based framework using dynamic programming (DP) to embed the structural
constraint to the Linear Quadratic Regulator (LQR) gain computation in the
continuous-time setting. Subsequently, we transform this LQR formulation into a
policy iteration RL algorithm that can alleviate the requirement of known state
matrix in conjunction with maintaining the feedback gain structure. Theoretical
guarantees are provided for stability and convergence of the structured RL
(SRL) algorithm. The introduced RL framework is general and can be applied to
any control structure. A special control structure enabled by this RL framework
is distributed learning control which is necessary for many large-scale
cyber-physical systems. As such, we validate our theoretical results with
numerical simulations on a multi-agent networked linear time-invariant (LTI)
dynamic system.
