Book Image

Deep Reinforcement Learning Hands-On. - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On. - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
26
Other Books You May Enjoy
27
Index

SAC

In the final section, we will check our environments on the latest state-of-the-art method, called SAC, which was proposed by a group of Berkeley researchers and introduced in the paper Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning, by Tuomas Taarnoja et. al. arXiv 1801.01290, published in 2018.

At the moment, it's considered to be one of the best methods for continuous control problems. The core idea of the method is closer to the DDPG method than to A2C policy gradients. The SAC method might have been more logically described in Chapter 17, Continuous Action Space. However, in this chapter, we have the chance to compare it directly with PPO's performance, which was considered to be the de facto standard in continuous control problems for a long time.

The central idea in the SAC method is entropy regularization, which adds a bonus reward at each timestamp that is proportional to the entropy of the policy at this timestamp. In mathematical...