Reinforcement Learning (RL) has emerged as a highly effective technique for
addressing various scientific and applied problems. Despite its success,
certain complex tasks remain challenging to be addressed solely with a single
model and algorithm. In response, ensemble reinforcement learning (ERL), a
promising approach that combines the benefits of both RL and ensemble learning
(EL), has gained widespread popularity. ERL leverages multiple models or
training algorithms to comprehensively explore the problem space and possesses
strong generalization capabilities. In this study, we present a comprehensive
survey on ERL to provide readers with an overview of recent advances and
challenges in the field. First, we introduce the background and motivation for
ERL. Second, we analyze in detail the strategies that have been successfully
applied in ERL, including model averaging, model selection, and model
combination. Subsequently, we summarize the datasets and analyze algorithms
used in relevant studies. Finally, we outline several open questions and
discuss future research directions of ERL. By providing a guide for future
scientific research and engineering applications, this survey contributes to
the advancement of ERL.Comment: 42 page