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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Despite its simplicity, and the toy-like example in this chapter, seq2seq is a very widely used model in NLP and other domains, so the alternative RL approach could potentially be applicable to a wide range of problems. In this chapter, we've just scratched the surface of deep NLP models and ideas, which goes well beyond the scope of this book. We covered the basics of NLP models, such as RNN and the seq2seq model and different ways that it could be trained.

In the next chapter, we'll take a look at another example of the application of RL methods in another domain: automating web navigation tasks.