综述强化学习在金融中的应用与挑战，指明未来研究方向。
A Review of Reinforcement Learning in Financial Applications
- 系统梳理强化学习在金融决策中的应用场景与方法
- 发现解释性、MDP建模和鲁棒性是主要瓶颈
- 适合对金融AI感兴趣的科研人员和从业者
近年来，强化学习（RL）在金融领域的应用日益增多，展现出解决金融决策任务的巨大潜力。本文全面综述了强化学习在金融中的应用，并通过一系列元分析探讨了文献中的共性主题，例如相较于传统方法，哪些因素最显著影响强化学习的性能。此外，文章识别出阻碍强化学习在金融行业更广泛应用的关键挑战，包括可解释性、马尔可夫决策过程（MDP）建模以及鲁棒性问题，并讨论了近期在克服这些挑战方面的进展。最后，提出了未来研究方向，如基准测试、上下文强化学习、多智能体强化学习和基于模型的强化学习，以应对现有挑战并进一步推动强化学习在金融领域的应用。
原文摘要 · Abstract (English)
In recent years, there has been a growing trend of applying Reinforcement Learning (RL) in financial applications. This approach has shown great potential to solve decision-making tasks in finance. In this survey, 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 to traditional methods. Moreover, we identify challenges including explainability, Markov Decision Process (MDP) 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.
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