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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Book summary

My congratulations on reaching the end of the book! I hope that the book was useful and you enjoyed reading it as much as I enjoyed gathering material and writing all the chapters. As a final word, I'd like to wish you good luck in this exciting and dynamic area of RL. The domain is developing very rapidly, but with an understanding of the basics, it becomes much simpler for you to keep track of the new developments and research in this field.

There are lots of very interesting topics left uncovered, such as partially observable MDPs (where environment observations don't fulfill the Markov property) or recent approaches to exploration, such as the count-based methods. There is a lot of recent activity around multi-agent methods, where many agents need to learn how to coordinate to solve a common problem. We also haven't mentioned the memory-based RL approach, where your agent can maintain some sort of a memory to keep its knowledge and experience. Lots of efforts are put into...