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)
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Index

The theoretical background of the cross-entropy method

This section is optional and included for readers who are interested in why the method works. If you wish, you can refer to the original paper on the cross-entropy method, which will be given at the end of the section.

The basis of the cross-entropy method lies in the importance sampling theorem, which states this:

In our RL case, H(x) is a reward value obtained by some policy, x, and p(x) is a distribution of all possible policies. We don't want to maximize our reward by searching all possible policies; instead we want to find a way to approximate p(x)H(x) by q(x), iteratively minimizing the distance between them. The distance between two probability distributions is calculated by Kullback-Leibler (KL) divergence, which is as follows:

The first term in KL is called entropy and it doesn't depend on p2(x), so it could be omitted during the minimization. The second term is called cross-entropy, which is...