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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Adding text descriptions

As the last example of this chapter, we will add text descriptions of the problem into observations of our model. I have already mentioned that some problems contain vital information given in a text description, like the index of tabs needed to be clicked or the list of entries that the agent needs to check. The same information is shown on top of the image observation, but pixels are not always the best representation of simple text.

To take this text into account, we need to extend our model's input from an image only to an image and text data. We worked with text in the previous chapter, so a recurrent neural network (RNN) is quite an obvious choice (maybe not the best for such a toy problem, but it is flexible and scalable).


I'm not going to cover this example in detail but will just focus on the most important points of the implementation. (The whole code is in Chapter16/ In comparison to our clicker...