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 5. Tabular Learning and the Bellman Equation

In the previous chapter, we got acquainted with our first Reinforcement Learning (RL) method, cross-entropy, and saw its strengths and weaknesses. In this new part of the book, we'll look at another group of methods, called Q-learning, which have much more flexibility and power.

This chapter will establish the required background shared by those methods. We'll also revisit the FrozenLake environment and show how new concepts will fit with this environment and help us to address the issues of the environment's uncertainty.