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)
Other Books You May Enjoy


To experiment with the methods in this chapter, we will use Roboschool, which uses PyBullet as a physics engine and has 13 environments of various complexity. PyBullet has similar environments, but at the time of writing, it isn't possible to create several instances of the same environment due to an internal OpenGL issue.

In this chapter, we will explore two problems: RoboschoolHalfCheetah-v1, which models a two-legged creature, and RoboschoolAnt-v1, which has four legs. Their state and action spaces are very similar to the Minitaur environment that we saw in Chapter 17, Continuous Action Space: the state includes characteristics from joints, and the actions are activations of those joints. The goal for both is to move as far as possible, minimizing the energy spent. Figure 19.1 shows screenshots of the two environments.

Figure 19.1: Screenshots of two Roboschool environments: RoboschoolHalfCheetah and RoboschoolAnt

To install Roboschool, you need to follow...