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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N-step DQN

The first improvement that we will implement and evaluate is quite an old one. It was first introduced in the paper Learning to Predict by the Methods of Temporal Differences, by Richard Sutton ([2] Sutton, 1988). To get the idea, let's look at the Bellman update used in Q-learning once again:

This equation is recursive, which means that we can express Q(st+1, at+1) in terms of itself, which gives us this result:

Value ra,t+1 means local reward at time t+1, after issuing action a. However, if we assume that action a at the step t+1 was chosen optimally, or close to optimally, we can omit the maxa operation and obtain this:

This value can be unrolled again and again any number of times. As you may guess, this unrolling can be easily applied to our DQN update by replacing one-step transition sampling with longer transition sequences of n-steps. To understand why this unrolling will help us to speed up training, let's consider the example illustrated...