Foundations of Deep Reinforcement Learning: Theory and Practice in Python Contributor(s): Graesser, Laura (Author), Keng, Wah Loon (Author) |
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ISBN: 0135172381 ISBN-13: 9780135172384 Publisher: Addison-Wesley Professional OUR PRICE: $47.49 Product Type: Paperback Published: December 2019 |
Additional Information |
BISAC Categories: - Computers | Databases - Data Mining - Computers | Intelligence (ai) & Semantics |
Series: Addison-Wesley Data & Analytics |
Physical Information: 0.5" H x 6.9" W x 9" (1.10 lbs) 416 pages |
Descriptions, Reviews, Etc. |
Publisher Description: The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games-such as Go, Atari games, and DotA 2-to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.
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