In recent years, there has been a growing trend of applying reinforcement learning (RL) in financial applications. This approach has shown great potential for decision-making tasks in finance. In this review, we present a comprehensive study of the applications of RL in finance and conduct a series of meta-analyses to investigate the common themes in the literature, such as the factors that most significantly affect RL's performance compared with traditional methods. Moreover, we identify challenges, including explainability, Markov decision process modeling, and robustness, that hinder the broader utilization of RL in the financial industry and discuss recent advancements in overcoming these challenges. Finally, we propose future research directions, such as benchmarking, contextual RL, multi-agent RL, and model-based RL to address these challenges and to further enhance the implementation of RL in finance.

中文翻译：

---

金融应用中的强化学习综述


近年来，在金融应用中应用强化学习 （RL） 的趋势越来越明显。这种方法在财务决策任务中显示出巨大的潜力。在这篇综述中，我们对 RL 在金融中的应用进行了全面研究，并进行了一系列荟萃分析以调查文献中的共同主题，例如与传统方法相比，对 RL 表现影响最显着的因素。此外，我们还确定了阻碍 RL 在金融行业更广泛使用的挑战，包括可解释性、马尔可夫决策过程建模和稳健性，并讨论了克服这些挑战的最新进展。最后，我们提出了未来的研究方向，如基准测试、情境 RL、多智能体 RL 和基于模型的 RL，以应对这些挑战并进一步加强 RL 在金融中的实施。