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


In this chapter, we covered three important approaches to solving MDPs: Dynamic programming, Monte Carlo methods, and temporal-difference learning. We have seen that while DP provides exact solutions to MDPs, it requires knowing the precise dynamics of an environment. Monte Carlo and TD learning methods, on the other hand, explore in the environment and learn from experience. TD learning, in particular, can learn from even a single step transitions in the environment. Within the chapter, we also presented on-policy methods, which estimate the value functions for a behavior policy, while off-policy methods for a target policy. Finally, we discussed the importance of the simulator in RL experiments and what to pay attention to when working with one.

Next, we take our journey to a next level and dive into deep reinforcement learning, which will enable us to solve some complex real-world problems. Particularly, in the next chapter, we cover deep Q-learning in detail.