Abstract
Reinforcement Learning (RL) has experienced significant advancement over the past decade, prompting a growing interest in applications within finance. This survey critically evaluates 167 publications, exploring diverse RL applications and frameworks in finance. Financial markets, marked by their complexity, multi-agent nature, information asymmetry, and inherent randomness, serve as an intriguing test-bed for RL. Traditional finance offers certain solutions, and RL advances these with a more dynamic approach, incorporating machine learning methods, including transfer learning, meta-learning, and multi-agent solutions. This survey dissects key RL components through the lens of Quantitative Finance. We uncover emerging themes, propose areas for future research, and critique the strengths and weaknesses of existing methods.
Abstract (translated)
强化学习（RL）在过去的十年里取得了显著的进展，这引起了对金融领域应用的浓厚兴趣。这项调查对167篇论文进行了审查，探讨了金融领域中多种RL应用和框架。金融市场以其复杂性、多代理性、信息不对称性和固有随机性而闻名，成为RL的一个有趣的实验平台。传统金融提供了一些解决方案，RL以更动态的方法推动这些解决方案，包括机器学习方法，包括迁移学习、元学习和支持性学习。通过量化金融的视角，我们剖析了RL的关键组成部分。我们发现了新兴的主题，提出了未来的研究方向，并批判了现有方法的优缺点。
URL
https://arxiv.org/abs/2408.10932
