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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Foundations of Deep Reinforcement Learning: Theory and Practice in Python

Foundations of Deep Reinforcement Learning: Theory and Practice in Python

Graesser, Laura
Keng, Wah Loon

Paperback

Series: Addison-Wesley Data & Analytics

DatabasesGeneral Computers

ISBN10: 0135172381
ISBN13: 9780135172384
Publisher: Addison-Wesley Professional
Published: Dec 5 2019
Pages: 416
Weight: 1.10
Height: 0.50 Width: 6.90 Depth: 9.00
Language: English
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.

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