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 have covered the mathematical framework in which we model the sequential decision-making problems we face in real-life: Markov decision processes. To this end, we started with Markov chains, which do not involve any concept of reward or decision making. Markov chains simply describe stochastic processes where the system transitions based on the current state and independent of the previously visited states. We then added the notion of a reward and started discussing things like which states are more advantageous to be in in terms of the expected future rewards. This created a concept of a "value" for a state. Then, we finally brought in the concept of "decision/action" and defined the Markov decision process. Subsequently, we finalized the definitions of state-value functions and action-value functions. Lastly, we discussed what a partially observable environment is and how it affects the decision-making of an agent.

The Bellman equation...