Artificial Intelligence in Finance, Volume 2: Reinforcement Learning Theory and Practice

Redefine What’s Possible in Finance with Reinforcement Learning.
In this second volume of the Artificial Intelligence in Finance series, the authors explore how Reinforcement Learning (RL) is transforming financial modelling, strategy, and decision-making.
At its core, RL allows models to learn from experience, dynamically adjusting their strategies based on feedback—successes or failures—at each step. This makes RL a game-changer for tackling multi-step, interdependent financial decisions and for designing entirely new algorithms to address unstructured, high-stakes challenges.
What You’ll Learn:
• Foundations first: Markov decision processes (MDPs), optimal policy learning, and general RL frameworks.
• Next-level techniques: Hybrid models that fuse RL with deep learning, stochastic approximation, temporal difference learning, and even large language models.
• Real-world impact:
o Portfolio and wealth management
o Algorithmic trading
o Options pricing and hedging
o Risk management and beyond
• State-of-the-art tools: Explore how Transformers and Graph Neural Networks handle complex financial datasets with unprecedented flexibility.
With a clear focus on practical application, this book blends rigorous theory with hands-on tools to help professionals and academics alike build smarter, more scalable financial systems.
Whether you're optimizing trading strategies, managing risk, or researching future-proof AI tools, this book is your roadmap to applying RL in the real world of finance.

Why This Book?
• Demystifies the power and limits of RL in finance
• Bridges the gap between academic theory and industry application
• Helps you build simulation-based, risk-aware, adaptive models
• Builds on Volume 1’s foundation—but stands strong on its own
For strategists, quants, data scientists, and curious minds—this is essential reading.
AI is changing finance. Reinforcement learning is leading the way.