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.

翻译：强化学习已在许多科学和应用问题中取得了最先进的性能。然而，一些复杂任务仍然难以通过单一模型和算法处理。广受欢迎的集成强化学习（ERL）结合了强化学习和集成学习（EL）的优势，已成为处理复杂任务的重要方法。ERL通过结合多个模型或训练算法来充分探索问题空间，并具有强大的泛化特性。本研究对ERL进行了全面综述，为读者提供近期进展与挑战的概述。首先介绍了背景知识，随后详细分析了在ERL中成功应用的策略。最后，我们指出了若干开放性问题，并讨论了ERL的未来研究方向。本综述通过为未来科学研究和工程应用提供指南，推动了ERL的发展。