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

Chapter 6: Deep Q-Learning at Scale

In the previous chapter, we covered dynamic programming (DP) methods to solve Markov decision processes, and then mentioned that they suffer two important limitations: DP i) assumes complete knowledge of the environment's reward and transition dynamics; ii) uses tabular representations of state and actions, which is not scalable as the number of possible state-action combinations is too big in many realistic applications. We have addressed the former by introducing the Monte Carlo (MC) and temporal-difference (TD) methods, which learn from their interactions with the environment (often in simulation) without needing to know the environment dynamics. On the other hand, the latter is yet to be addressed, and this is where deep learning comes in. Deep reinforcement learning (deep RL or DRL) is about utilizing neural networks' representational power to learn policies for a wide variety of situations.

As great as it sounds, though, it is...