Abstract
Deep Reinforcement Learning (DRL) and Evolution Strategies (ESs) have
surpassed human-level control in many sequential decision-making
problems, yet many open challenges still exist.
To get insights into the strengths and weaknesses of DRL versus ESs, an
analysis of their respective capabilities and limitations is provided.
After presenting their fundamental concepts and algorithms, a comparison
is provided on key aspects such as scalability, exploration, adaptation
to dynamic environments, and multi-agent learning.
Then, the benefits of hybrid algorithms that combine concepts from DRL
and ESs are highlighted.
Finally, to have an indication about how they compare in real-world
applications, a survey of the literature for the set of applications
they support is provided.