Book Image

Reinforcement Learning Algorithms with Python

By : Andrea Lonza
Book Image

Reinforcement Learning Algorithms with Python

By: Andrea Lonza

Overview of this book

Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents. Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods, such as the application of Q-learning and SARSA algorithms. You'll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore, you'll study the policy gradient methods, TRPO, and PPO, to improve performance and stability, before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to drive. You'll discover evolutionary strategies and black-box optimization techniques, and see how they can improve RL algorithms. Finally, you'll get to grips with exploration approaches, such as UCB and UCB1, and develop a meta-algorithm called ESBAS. By the end of the book, you'll have worked with key RL algorithms to overcome challenges in real-world applications, and be part of the RL research community.
Table of Contents (19 chapters)
Free Chapter
1
Section 1: Algorithms and Environments
5
Section 2: Model-Free RL Algorithms
11
Section 3: Beyond Model-Free Algorithms and Improvements
17
Assessments

Categorizing RL algorithms

Before deep diving into the first RL algorithm that solves the optimal Bellman equation, we want to give a broad but detailed overview of RL algorithms. We need to do this because their distinctions can be quite confusing. There are many parts involved in the design of algorithms, and many characteristics have to be considered before deciding which algorithm best fits the actual needs of the user. The scope of this overview presents the big picture of RL so that in the next chapters, where we'll give a comprehensive theoretical and practical view of these algorithms, you will already see the general objective and have a clear idea of their location in the map of RL algorithms.

The first distinction is between model-based and model-free algorithms. As the name suggests, the first requires a model of the environment, while the second is free from...