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)
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The TextWorld Environment

In the previous chapter, you saw how reinforcement learning (RL) methods can be applied to natural language processing (NLP) problems, in particular, to improve the chatbot training process. Continuing our journey into the NLP domain, in this chapter, we will now use RL to solve text-based interactive fiction games, using the environment published by Microsoft Research called TextWorld.

In this chapter, we will:

  • Cover a brief historical overview of interactive fiction
  • Study the TextWorld environment
  • Implement the simple baseline deep Q-network (DQN) method, and then try to improve it by implementing a command generator using recurrent neural networks (RNNs). This will provide a good illustration of how RL can be applied to complicated environments with a rich observation space