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

REINFORCE issues

In the previous section, we discussed the REINFORCE method, which is a natural extension of the cross-entropy method. Unfortunately, both REINFORCE and the cross-entropy method still suffer from several problems, which make both of them limited to simple environments.

Full episodes are required

First of all, we still need to wait for the full episode to complete before we can start training. Even worse, both REINFORCE and the cross-entropy method behave better with more episodes used for training (just from the fact that more episodes mean more training data, which means more accurate policy gradients). This situation is fine for short episodes in the CartPole, when in the beginning, we can barely handle the bar for more than 10 steps; but in Pong, it is completely different: every episode can last for hundreds or even thousands of frames. It's equally bad from the training perspective, as our training batch becomes very large, and from the sample efficiency...