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

DQN on Pong

Before we jump into the code, some introduction is needed. Our examples are becoming increasingly challenging and complex, which is not surprising, as the complexity of the problems that we are trying to tackle is also growing. The examples are as simple and concise as possible, but some of the code may be difficult to understand at first.

Another thing to note is performance. Our previous examples for FrozenLake, or CartPole, were not demanding from a performance perspective, as observations were small, NN parameters were tiny, and shaving off extra milliseconds in the training loop wasn't important. However, from now on, that's not the case. One single observation from the Atari environment is 100k values, which have to be rescaled, converted to floats, and stored in the replay buffer. One extra copy of this data array can cost you training speed, which will not be seconds and minutes anymore, but could be hours on even the fastest graphics processing unit...