  Continuous action policy search is currently the focus of intensive research,
driven both by the recent success of deep reinforcement learning algorithms and
the emergence of competitors based on evolutionary algorithms. In this paper,
we present a broad survey of policy search methods, providing a unified
perspective on very different approaches, including also Bayesian Optimization
and directed exploration methods. The main message of this overview is in the
relationship between the families of methods, but we also outline some factors
underlying sample efficiency properties of the various approaches.
