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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Things to try

In this chapter, we only started playing with MiniWoB by touching upon the six easiest environments from the full set of 80 problems, so there is plenty of uncharted territory ahead. If you want to practice, there are several items you can experiment with:

  • Testing the robustness of demonstrations to noisy clicks.
  • Implementing training of the value head of A3C based on demonstration data.
  • Implementing more sophisticated mouse control, like move mouse N pixels left/right/top/bottom.
  • Using some pretrained optical character recognition (OCR) network (or training your own!) to extract text information from the observations.
  • Taking other problems and trying to solve them. There are some quite tricky and fun problems, like sort items using drag-n-drop or repeat the pattern using checkboxes.
  • Checking MiniWoB++ ( from the Stanford NLP Group. It will require learning and writing new wrappers; as mentioned...