Deep reinforcement learning in action
Material type:
TextPublication details: Shelter Island, NY : Manning Publications Company, 2020 Description: xx, 359 pages ; 23 cmISBN: 9781617295430Subject(s): Machine learning | Algorithms | Reinforcement learning | Computational learning theory | Artificial intelligenceDDC classification: 004.85 Online resources: Reviews Summary: Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym.
| Item type | Current library | Call number | Status | Date due | Barcode |
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Book
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NISER LIBRARY | 004.85 ZAI-D (Browse shelf(Opens below)) | Available | 25564 |
Includes bibliographical references (pages 348-350) and index.
Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym.
For readers with intermediate skills in Python and deep learning.
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