You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook

Published inJan 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838826994

Edition2nd Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Deep Reinforcement Learning

Author (1)

Maxim Lapan

Summary

In this chapter, you saw several ways to improve the performance of the RL method using a pure engineering approach, which was in contrast to the "algorithmic" or "theoretical" approach covered in Chapter 8, DQN Extensions. From my perspective, both approaches complement each other, and a good RL practitioner needs to both know the latest tricks that researchers have found and be aware of the implementation details.

In the next chapter, we will apply our DQN knowledge to stocks trading.

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Deep Reinforcement Learning Hands-On. - Second Edition

Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994

Author (1)

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Summary

Unlock this book and the full library FREE for 7 days

Author (1)

TensorFlow Reinforcement Learning Quick Start Guide

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