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)
Other Books You May Enjoy

Advanced Exploration

Next, we will talk about the topic of exploration in reinforcement learning (RL). It has been mentioned several times in the book that the exploration/exploitation dilemma is a fundamental thing in RL and very important for efficient learning. However, in the previous examples, we used quite a trivial approach to exploring the environment, which was, in most cases, -greedy action selection. Now it's time to go deeper into the exploration subfield of RL.

In this chapter, we will:

  • Discuss why exploration is such a fundamental topic in RL
  • Explore the effectiveness of the epsilon-greedy (-greedy) approach
  • Take a look at alternatives and try them on different environments