Grokking Deep Reinforcement Learning

Morales, Miguel

ISBN 10: 1617295450 / ISBN 13: 9781617295454

Published by Manning Publications, 2020

About this Item

450 pages. 9.25x7.25x1.00 inches. In Stock.

Seller Inventory # zk1617295450

Bibliographic Details

• Title: Grokking Deep Reinforcement Learning
• Publisher: Manning Publications
• Publication Date: 2020
• Language: English
• ISBN 10: 1617295450
• ISBN 13: 9781617295454
• Binding: Paperback
• Condition: Brand New
• Weight: 0.77 kilograms

About this title

Synopsis

Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required.

 

Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.

 

We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment.

 

• Foundational reinforcement learning concepts and methods

• The most popular deep reinforcement learning agents solving high-dimensional environments

• Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence

 

Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return.

About the Author

Miguel Morales is a Senior Software Engineer at Lockheed Martin, Missile and Fire Control-Autonomous Systems. He is also a faculty member at Georgia Institute of Technology where he works as an Instructional Associate for the Reinforcement Learning and Decision Making graduate course. Miguel has worked for numerous other educational and technology companies including Udacity, AT&T, Cisco, and HPE.