Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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Chapter 4. The Cross-Entropy Method

In this chapter, we will wrap up the part one of the book and get familiar with one of the RL methods—cross-entropy. Despite the fact that it is much less famous than other tools in the RL practitioner's toolbox, such as deep Q-network (DQN) or Advantage Actor-Critic, this method has its own strengths. The most important are as follows:

  • Simplicity: The cross-entropy method is really simple, which makes it an intuitive method to follow. For example, its implementation on PyTorch is less than 100 lines of code.

  • Good convergence: In simple environments that don't require complex, multistep policies to be learned and discovered and have short episodes with frequent rewards, cross-entropy usually works very well. Of course, lots of practical problems don't fall into this category, but sometimes they do. In such cases, cross-entropy (on its own or as a part of a larger system) can be the perfect fit.

In the following sections, we will start from the practical...