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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In this chapter, we started our journey into the RL world by learning what makes RL special and how it relates to the supervised and unsupervised learning paradigm. We then learned about the basic RL formalisms and how they interact with each other, after which we defined Markov process, Markov reward process, and Markov decision process.

In the next chapter, we'll move away from the formal theory into the practice of RL. We'll cover the setup required, libraries, and write our first agent.