Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Graesser, Laura; Keng, Wah Loon
Language: English
Published by Addison-Wesley Professional, 2019
- Softcover
- Used
Condition: Used - As new
US$ 39.83
Quantity: 1 available
Add to basketItem description from seller
Complete and unmarked. All pages present and readable, binding tight. No highlighting, underlining, or writing anywhere in the text. Cover and spine show minimal to no shelf wear. May have a remainder mark, a price sticker or its residue, or a previous owner's name on the inside cover. Access codes, CDs, DVDs, and other bundled supplements may not be included and should be assumed used or missing unless the listing explicitly states otherwise.
Seller Inventory # B4320C00986U
- Title
- Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
- Author
- Graesser, Laura; Keng, Wah Loon
- Publisher
- Addison-Wesley Professional
- Publication year
- 2019
- Condition
- As New
- Binding
- paperback
- Language
- English
- ISBN 10
- 0135172381
- ISBN 13
- 9780135172384
- Item weight
- 19 ounces
- Dimensions
- 6x0x9
- Series
- Book 8 of 14: Addison-Wesley Data & Analytics
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.
- 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
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
Shipping rates within U.S.A.
| Item | 4 to 10 business days | 2 to 5 business days |
|---|---|---|
| First item | US$ 0.00 | US$ 3.99 |
Payment methods
Store description
Top quality textbooks! Unbeatable prices!
Specialty
Mostly used textbooksSeller's business information
FREESIA LLC
2000 E 42ND ST STE C-351
ODESSA, TX U.S.A. 79762
Terms of sale
30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.