Practical Deep Reinforcement Learning

Developers teaching software agents to learn from their environment, from walking robots, talking avatars and playing games, to rewriting programs.
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Practical Deep Reinforcement Learning

Maxim Lapan

Developers teaching software agents to learn from their environment, from walking robots, talking avatars and playing games, to rewriting programs.

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Book Details

ISBN 139781788834247
Paperback279 pages

Book Description

Reinforcement Learning (RL) is much more than the newest buzzword in deep learning. Like most areas in machine learning, the first popular texts have been around since the late 90s, but it is only since Google started to use RL algorithms to play and defeat well-known computer games, that the field shot to prominence.

This is the first book to present RL from the first principles. It presents RL algorithms and methods developed since the late 90s, in an accessible and practical fashion. RL stands for the art of coding intelligent learning agents able to adapt to a formidable array of tasks.

Max Lapan leads you through some well-known areas such as the Bellman equation and dynamic programming, and also introduces Deep-Q Network problems and Policy Gradient approaches in some depth. Max ends with a ride through some of the recent developments in RL, suggesting applications and new departures.

Table of Contents

What You Will Learn

  • Understand the deep learning context of RL
  • See how to implement simple RL techniques such as the Bellman equation
  • Apply Policy Gradient approaches to the real world
  • Defeat computer games without ever touching a keyboard
  • Learn the required deep learning and machine learning methods to understand RL

Authors

Table of Contents

Book Details

ISBN 139781788834247
Paperback279 pages
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