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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Trust Regions – PPO, TRPO, ACKTR, and SAC

Next, we will take a look at the approaches used to improve the stability of the stochastic policy gradient method. Some attempts have been made to make the policy improvement more stable, and in this chapter, we will focus on three methods:

  • Proximal policy optimization (PPO)
  • Trust region policy optimization (TRPO)
  • Advantage actor-critic (A2C) using Kronecker-factored trust region (ACKTR).

In addition, we will compare those methods to a relatively new off-policy method called soft actor-critic (SAC), which is a development of the deep deterministic policy gradients (DDPG) method described in Chapter 17, Continuous Action Space. To compare them to the A2C baseline, we will use several environments from the Roboschool library created by OpenAI.

The overall motivation of the methods that we will look at is to improve the stability of the policy update during the training. There is a dilemma: on the one hand, we...