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.

翻译：强化学习（RL）已成为解决各类科学与应用问题的高效技术手段。尽管取得显著成功，但某些复杂任务仍难以仅靠单一模型和算法解决。为此，集成强化学习（ERL）作为融合强化学习与集成学习（EL）优势的前瞻性方法，获得了广泛关注。ERL通过利用多个模型或训练算法全面探索问题空间，具备强大的泛化能力。本文对ERL进行了系统性综述，旨在为读者呈现该领域的最新进展与挑战。首先，我们阐述了ERL的背景与动机；其次，详细分析了ERL中成功应用的策略，包括模型平均、模型选择及模型组合；随后，梳理了相关研究中的数据集与算法分析；最后，总结了若干开放性问题并探讨了ERL的未来研究方向。通过为未来科学研究与工程应用提供指导，本综述将推动ERL领域的进展。