Foundations of Deep Reinforcement Learning: Theory and Practice in Python
Graesser, Laura; Keng, Wah Loon
Language: English
Published by Addison-Wesley Professional, 2019
- Softcover
- Used
Seller: ThriftBooks-Dallas, Dallas, TX, U.S.A.ThriftBooks-Dallas
AbeBooks seller since July 2, 2009
Condition: Used - Good
US$ 39.43
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Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
Seller Inventory # G0135172381I3N00
- Title
- Foundations of Deep Reinforcement Learning: Theory and Practice in Python
- Author
- Graesser, Laura; Keng, Wah Loon
- Publisher
- Addison-Wesley Professional
- Publication year
- 2019
- Condition
- Good
- Dust jacket
- No Jacket
- Binding
- Paperback
- Language
- English
- ISBN 10
- 0135172381
- ISBN 13
- 9780135172384
- Item weight
- 1.01 pounds
- 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
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ThriftBooks-Dallas
Dallas, TX, U.S.A.
AbeBooks seller since July 2, 2009
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