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)
1
Section 1: Reinforcement Learning Foundations
7
Section 2: Deep Reinforcement Learning
12
Section 3: Advanced Topics in RL
17
Section 4: Applications of RL

Exploring the challenges in multi-agent reinforcement learning

In the earlier chapters in this book, we discussed many challenges in reinforcement learning. In particular, the dynamic programming methods we initially introduced are not able to scale to problems with complex and large observation and action spaces. Deep reinforcement learning approaches, on the other hand, although capable of handling complex problems, lack theoretical guarantees and therefore required many tricks to stabilize and converge. Now that we talk about problems in which there are more than one agent learning, interacting with each other, and affecting the environment; the challenges and complexities of single-agent RL are multiplied. For this reason, many results in MARL are empirical.

In this section, we discuss what makes MARL specifically complex and challenging.

Non-stationarity

The mathematical framework behind single-agent RL is the Markov decision process (MDP), which establishes that the...