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, we saw a practical example of RL and implemented the trading agent and custom Gym environment. We tried two different architectures: a feed-forward network with price history on input and a 1D convolution network. Both architectures used the DQN method, with some extensions described in Chapter 8, DQN Extensions.

This is the last chapter in part two of this book. In part three, we will talk about a different family of RL methods: policy gradients. We've touched on this approach a bit, but in the upcoming chapters, we will go much deeper into the subject, covering the REINFORCE method and the best method in the family: A3C.

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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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