Book Image

Mastering Reinforcement Learning with Python

By : Enes Bilgin
Book Image

Mastering Reinforcement Learning with Python

By: Enes Bilgin

Overview of this book

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.
Table of Contents (24 chapters)
Section 1: Reinforcement Learning Foundations
Section 2: Deep Reinforcement Learning
Section 3: Advanced Topics in RL
Section 4: Applications of RL

Applying reinforcement learning in finance

If we need to reiterate RL's promise, it is to obtain policies for sequential decision making to maximize rewards under uncertainty. What is a better match than finance for such a tool? In finance:

  • The goal is very much to maximize some monetary reward,
  • Decisions made now will definitely have consequences down the road,
  • Uncertainty is a defining factor.

As a result, RL is getting increasingly popular in the finance community.

To be clear, this section will not include any examples of a winning trading strategy, well, for obvious reasons: First, the author does not know any; second, even if he did, he would not tell that in a book (and no one would). In addition, there are challenges when it comes to using RL in finance. So, we start this section with a discussion on these challenges. Once we ground ourselves in reality, then we will proceed to defining some application areas and introducing some tools you can...