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

Introducing model-based methods

Imagine a scene in which you are traveling in a car on an undivided road and you face the following situation. Suddenly, another car in the opposing direction approaches you fast in your lane as it is passing a truck. Chances are your mind automatically simulates different scenarios about how the next scenes might unfold:

  • The other car might go back to its lane right away or drive even faster to pass the truck as soon as possible.
  • Another scenario could be the car steering toward your right, but this is an unlikely scenario (in a right-hand traffic flow).

The driver (possibly you) then evaluates the likelihood and risk of each scenario, together with their possible actions too, and makes the decision to safely continue the journey.

In a less sensational example, consider a game of chess. Before making a move, a player "simulates" many scenarios in their head and assesses the possible outcomes of several moves down...