Reinforcement Learning in Digital Finance offers a comprehensive exploration of reinforcement learning (RL), which is a powerful framework to support sequential decision-making under uncertainty.

Candidates will delve into the theoretical foundations of reinforcement learning, state-of-the-art algorithms, and practical applications within the digital finance domain. RL can facilitate and enhance sequential decision-making processes and services in finance, such as algorithmic trading, portfolio optimization, risk management, and fraud detection. Doctoral candidates will gain a solid understanding of the theoretical underpinnings of RL as well as practical experience in implementing reinforcement learning algorithms to solve real-world financial problems. The applications will consider constraints and regulations that concern privacy, algorithmic bias, and explainability.

By the end of the course, candidates will be equipped with the knowledge and skills to leverage reinforcement learning for optimizing control, enhancing performance, and improving services in the dynamic landscape of digital finance.