Abstract
Deep Reinforcement Learning (DRL) has the potential to surpass
human-level control in sequential decision-making problems. Evolution
Strategies (ESs) have different characteristics than DRL, yet they are
promoted as a scalable alternative.
To get insights into their strengths and weaknesses, in this paper, we
put the two approaches side by side. After presenting the fundamental
concepts and algorithms for each of the two approaches, they are
compared from the perspectives of scalability, exploration, adaptation
to dynamic environments, and multi-agent learning. Then, the paper
discusses hybrid algorithms, combining aspects of both DRL and ESs, and
how they attempt to capitalize on the benefits of both techniques.
Lastly, both approaches are compared based on the set of applications
they support, showing their potential for tackling real-world problems.
This paper aims to present an overview of how DRL and ESs can be used,
either independently or in unison, to solve specific learning tasks. It
is intended to guide researchers to select which method suits them best
and provides a bird’s eye view of the overall literature in the field.
Further, we also provide application scenarios and open challenges.