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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Several environments

The first idea that we usually apply to speed up deep learning training is larger batch size. It's applicable to the domain of deep RL, but you need to be careful here. In the normal supervised learning case, the simple rule "a large batch is better" is usually true: you just increase your batch as your GPU memory allows, and a larger batch normally means more samples will be processed in a unit of time thanks to enormous GPU parallelism.

The RL case is slightly different. During the training, two things happen simultaneously:

  • Your network is trained to get better predictions on the current data
  • Your agent explores the environment

As the agent explores the environment and learns about the outcome of its actions, the training data changes. In a shooter example, your agent can run randomly for a time while being shot by monsters and have only a miserable "death is everywhere" experience in the training buffer. But after...