Ensemble Reinforcement Learning: A Survey

Reinforcement learning (RL) has achieved state-of-the-art performance in many scientific and applied problems. However, some complex tasks still are difficult to handle using a single model and algorithm. The highly popular ensemble reinforcement learning (ERL) has become an important method to handle complex tasks with the advantage of combining reinforcement learning and ensemble learning (EL). ERL combines several models or training algorithms to fully explore the problem space and has strong generalization characteristics. This study presents a comprehensive survey on ERL to provide the readers with an overview of the recent advances and challenges. The background is introduced first. The strategies successfully applied in ERL are analyzed in detail. Finally, we outline some open questions and conclude by discussing some future research directions of ERL. This survey contributes to ERL development by providing a guide for future scientific research and engineering applications.