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

Reinforcement learning

RL is the third camp and lies somewhere in between full supervision and a complete lack of predefined labels. On the one hand, it uses many well-established methods of supervised learning, such as deep neural networks for function approximation, stochastic gradient descent, and backpropagation, to learn data representation. On the other hand, it usually applies them in a different way.

In the next two sections of the chapter, we will explore specific details of the RL approach, including assumptions and abstractions in its strict mathematical form. For now, to compare RL with supervised and unsupervised learning, we will take a less formal, but more easily understood, path.

Imagine that you have an agent that needs to take actions in some environment. (Both "agent" and "environment" will be defined in detail later in this chapter.) A robot mouse in a maze is a good example, but you can also imagine an automatic helicopter trying to perform...

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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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