A survey on Evolutionary Reinforcement Learning algorithms.

Reinforcement Learning (RL) has proven to be highly effective in various real-world applications. However, in certain scenarios, Evolutionary Algorithms (EAs) have been utilized as an alternative to RL algorithms. Recently, Evolutionary Reinforcement Learning algorithms (ERLs) have emerged as a promising solution that combines the advantages of both RL and EA. This paper presents a comprehensive survey that encompasses a majority of the studies in this exciting research area. We classify these ERLs according to the EA used in their frameworks and analyze the strengths and limitations of various EA components and combination schemes. Additionally, we conduct several experiments to evaluate the performance of some representative ERLs. By categorizing the different approaches and assessing their effectiveness, the paper can assist researchers and practitioners in selecting the most suitable method for their particular application. • This paper provides an extensive survey of ERLs. • The paper classifies ERLs by their frameworks and evaluates strengths and limitations. • Various experiments showcase the methods' performance across diverse environments. [ABSTRACT FROM AUTHOR]