  Deep neuroevolution and deep Reinforcement Learning have received a lot of
attention in the last years. Some works have compared them, highlighting theirs
pros and cons, but an emerging trend consists in combining them so as to
benefit from the best of both worlds. In this paper, we provide a survey of
this emerging trend by organizing the literature into related groups of works
and casting all the existing combinations in each group into a generic
framework. We systematically cover all easily available papers irrespective of
their publication status, focusing on the combination mechanisms rather than on
the experimental results. In total, we cover 45 algorithms more recent than
2017. We hope this effort will favor the growth of the domain by facilitating
the understanding of the relationships between the methods, leading to deeper
analyses, outlining missing useful comparisons and suggesting new combinations
of mechanisms.
