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

Training policies in multi-agent settings

There are many algorithms and approaches designed for MARL, which can be classified in the following two broad categories.

  • Independent learning: This approach suggests training agents individually while treating the other agents in the environment as part of the environment.
  • Centralized training and decentralized execution: In this approach, there is a centralized controller that uses information from multiple agents during training. At the time of execution (inference), the agents locally execute the policies, without relying on a central mechanism.

Generally speaking, we can take any of the algorithms we covered in one of the previous chapters and use it in a multi-agent setting to train policies via independent learning, which, as it turns out, is a very competitive alternative to specialized MARL algorithms. So rather than dumping more theory and notation on you, in this chapter, we will skip discussing the technical...