  This research reports on the recent development of a black-box optimization
method based on single-step deep reinforcement learning (DRL), and on its
conceptual proximity to evolution strategy (ES) techniques. In the fashion of
policy gradient (PG) methods, the policy-based optimization (PBO) algorithm
relies on the update of a policy network to describe the density function of
its next generation of individuals. The method is described in details, and its
similarities to both ES and PG methods are pointed out. The relevance of the
approach is then evaluated on the minimization of standard analytic functions,
with comparison to classic ES techniques (ES, CMA-ES). It is then applied to
the optimization of parametric control laws designed for the Lorenz attractor.
Given the scarce existing literature on the method, this contribution
definitely establishes the PBO method as a valid, versatile black-box
optimization technique, and opens the way to multiple future improvements
allowed by the flexibility of the neural networks approach.
