Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

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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Index

The Actor-Critic Method

In Chapter 11, Policy Gradients—an Alternative, we started to investigate a policy-based alternative to the familiar value-based methods family. In particular, we focused on the method called REINFORCE and its modification, which uses discounted reward to obtain the gradient of the policy (which gives us the direction in which to improve the policy). Both methods worked well for a small CartPole problem, but for a more complicated Pong environment, the convergence dynamics were painfully slow.

Next, we will discuss another extension to the vanilla policy gradient method, which magically improves the stability and convergence speed of that method. Despite the modification being only minor, the new method has its own name, actor-critic, and it's one of the most powerful methods in deep reinforcement learning (RL).

In this chapter, we will:

  • Explore how the baseline impacts statistics and the convergence of gradients
  • Cover an extension...