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
Contributors
Preface
Other Books You May Enjoy
Index

The extra Gym functionality – wrappers and monitors


What we discussed so far covers two-thirds of the Gym core API and the essential functions required to start writing agents. The rest of the API you can live without, but it will make your life easier and your code cleaner. So, let's look at a quick overview of the rest of the API.

Wrappers

Very frequently, you will want to extend the environment's functionality in some generic way. For example, an environment gives you some observations, but you want to accumulate them in some buffer and provide to the agent the N last observations, which is a common scenario for dynamic computer games, when one single frame is just not enough to get the full information about the game state. Another example is when you want to be able to crop or preprocess an image's pixels to make it more convenient for the agent to digest or if you want to normalize reward scores somehow. There are many such situations that have the same structure: you'd like to "wrap...