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 6. Deep Q-Networks

In the previous chapter, we became familiar with the Bellman equation and the practical method of its application called Value iteration. This approach allowed us to significantly improve our speed and convergence in the FrozenLake environment, which is promising, but can we go further?

In this chapter, we'll try to apply the same theory to problems of much greater complexity: arcade games from the Atari 2600 platform, which are the de-facto benchmark of the RL research community. To deal with this new and more challenging goal, we'll talk about problems with the Value iteration method and introduce its variation, called Q-learning. In particular, we'll look at the application of Q-learning to so-called "grid world" environments, which is called tabular Q-learning, and then we'll discuss Q-learning in conjunction with neural networks. This combination has the name DQN. At the end of the chapter, we'll reimplement a DQN algorithm from the famous paper, Playing Atari...