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
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Index

The Cross-Entropy Method

In the last chapter, you got to know PyTorch. In this chapter, we will wrap up part one of this book and you will become familiar with one of the reinforcement learning (RL) methods: cross-entropy.

Despite the fact that it is much less famous than other tools in the RL practitioner's toolbox, such as deep Q-network (DQN) or advantage actor-critic, the cross-entropy method has its own strengths. Firstly, the cross-entropy method is really simple, which makes it an easy method to follow. For example, its implementation on PyTorch is less than 100 lines of code.

Secondly, the method has good convergence. In simple environments that don't require complex, multistep policies to be learned and discovered, and that have short episodes with frequent rewards, the cross-entropy method usually works very well. Of course, lots of practical problems don't fall into this category, but sometimes they do. In such cases, the cross-entropy method (on its...