  Policy optimization (PO), an essential approach of reinforcement learning for
a broad range of system classes, requires significantly more system data than
indirect (identification-followed-by-control) methods or behavioral-based
direct methods even in the simplest linear quadratic regulator (LQR) problem.
In this paper, we take an initial step towards bridging this gap by proposing
the data-enabled policy optimization (DeePO) method, which requires only a
finite number of sufficiently exciting data to iteratively solve the LQR
problem via PO. Based on a data-driven closed-loop parameterization, we are
able to directly compute the policy gradient from a batch of persistently
exciting data. Next, we show that the nonconvex PO problem satisfies a
projected gradient dominance property by relating it to an equivalent convex
program, leading to the global convergence of DeePO. Moreover, we apply
regularization methods to enhance certainty-equivalence and robustness of the
resulting controller and show an implicit regularization property. Finally, we
perform simulations to validate our results.
