Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Keng, Wah Loon,Graesser, Laura
7 ratings by Goodreads
Language: English
Published by Addison-Wesley Professional, 2019
- Softcover
- Used
Seller: HPB-Red, Dallas, TX, U.S.A.HPB-Red
5-star seller
AbeBooks seller since March 11, 2019
Softcover
Condition: Used - Good
US$ 28.80
US$ 3.75 shipping
Ships within U.S.A.
Quantity: 1 available
Add to basketFree 30-day returns
Item description from seller
Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.
Seller Inventory # S_408192872
- Title
- Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
- Author
- Keng, Wah Loon,Graesser, Laura
- Publisher
- Addison-Wesley Professional
- Publication year
- 2019
- Condition
- Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 0135172381
- ISBN 13
- 9780135172384
- Series
- Book 8 of 14: Addison-Wesley Data & Analytics
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.
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.
- Understand each key aspect of a deep RL problem
- Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)
- Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)
- Understand how algorithms can be parallelized synchronously and asynchronously
- Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work
- Explore algorithm benchmark results with tuned hyperparameters
- Understand how deep RL environments are designed
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
"Synopsis" may belong to another edition of this title.
About the Author
Laura Graesser is a research software engineer working in robotics at Google. She holds a master’s degree in computer science from New York University, where she specialized in machine learning.
Wah Loon Keng is an AI engineer at Machine Zone, where he applies deep reinforcement learning to industrial problems. He has a background in both theoretical physics and computer science.
"About the title" may belong to another edition of this title.
Shipping rates within U.S.A.
| Item | 4 to 14 business days | 2 to 6 business days |
|---|---|---|
| First item | US$ 3.75 | US$ 6.99 |
Payment methods
Store description
Half Price Books has been connecting readers to great books since 1972. Customer service is our top priority.
Specialty
AllSeller's business information
Half Price Books, Records, Magazines, Inc.
5803 E. Northwest Hwy.
Dallas, TX U.S.A. 75231