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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27
Index

Correlation and sample efficiency

One of the approaches to improving the stability of the policy gradient family of methods is using multiple environments in parallel. The reason behind this is the fundamental problem we discussed in Chapter 6, Deep Q-Networks, when we talked about the correlation between samples, which breaks the independent and identically distributed (i.i.d.) assumption, which is critical for stochastic gradient descent (SGD) optimization. The negative consequence of such correlation is very high variance in gradients, which means that our training batch contains very similar examples, all of them pushing our network in the same direction.

However, this may be totally the wrong direction in the global sense, as all those examples may be from one single lucky or unlucky episode.

With our deep Q-network (DQN), we solved the issue by storing a large number of previous states in the replay buffer and sampling our training batch from this buffer. If the buffer...