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

Best practices of deep RL

Throughout this book, we covered plenty of reinforcement learning algorithms, some of which are only upgrades (for example TD3, A2C, and so on), while others were fundamentally different from the others (such as TRPO and DPG) and propose an alternative way to reach the same objective. Moreover, we addressed non-RL optimization algorithms such as imitation learning and evolution strategies to solve sequential decision-making tasks. All of these alternatives may have created confusion and you may not know exactly which algorithm is best for a particular problem. If that is the case, don't worry, as we'll now go through some rules that you can use in order to decide which is the best algorithm to use for a given task.

Also, if you implemented some of the algorithms we went through in this book, you might find it hard to put all the pieces together...