Artificial Intelligence in Finance, Volume 2: Reinforcement Learning Theory and Practice
First published:
ISBN: 9781782724544
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Introduction
Miquel Noguer i Alonso, Daniel Bloch and David Pacheco Aznar
Introduction
Preface
Introduction
Markov decision problems
Learning the optimal policy
Reinforcement learning revisited
Temporal difference learning revisited
Stochastic approximation in Markov decision processes
Large language models: reasoning and reinforcement learning
Deep reinforcement learning
Applications of artificial intelligence in finance
Pricing options with temporal difference backpropagation
Pricing American options
Daily price limits
Portfolio optimisation
Appendix
Volume 1 of Artificial Intelligence in Finance offered a rich, detailed exploration of how artificial intelligence (AI) technologies intersect with the financial industry, while this volume provides more technical information on machine learning, as well as applications. In this chapter we describe the format of this volume.
1.1 MARKOV DECISION PROBLEMS
Chapter 2, on Markov decision problems (MDPs), delves into the mathematical framework used to model decision-making in reinforcement learning and other AI contexts. We begin by defining MDPs and exploring their key components, including states, actions, transition probabilities and rewards. We discuss how MDPs represent sequential decision-making problems where the outcomes are partly random and partly under the control of a decision maker. The stochastic nature of MDPs is explained, emphasising how decisions made in one state affect the trajectory of the system over time.
We outline different types of MDPs, including those with discrete and continuous time, and we examine the role of the Bellman equations in solving MDPs. This chapter provides a comprehensive overview of the credit assignment problem, a critical aspect of
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