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

The simple clicking approach

As the first demo, let's implement a simple A3C agent that decides where it should click given the image observation. This approach can solve only a small subset of the full MiniWoB suite, and we will discuss restrictions of this approach later. For now, it will allow us to get a better understanding of the problem.

As with the previous chapter, due to its size, I won't put the complete source code here. We will focus on the most important functions and I will provide the rest as an overview. The complete source code is available in the GitHub repository.

Grid actions

When we talked about Universe's architecture and organization, it was mentioned that the richness and flexibility of the action space creates a lot of challenges for the RL agent. MiniWoB's active area inside the browser is just 160×210 (exactly the same dimension that the Atari emulator has), but even with such a small area, our agent could be asked to move...