This book discusses the acceleration of deep reinforcement learning (DRL), which may be the next step in the burst success of artificial intelligence (AI). The authors address acceleration systems which enable DRL on area-limited & battery-limited mobile devices. Methods are described that enable DRL optimization at the algorithm-, architecture-, and circuit-levels of abstraction.
Deep Reinforcement Learning Processor Design for Mobile Applications
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$268.00
Description
This book discusses the acceleration of deep reinforcement learning (DRL), which may be the next step in the burst success of artificial intelligence (AI). The authors address acceleration systems which enable DRL on area-limited & battery-limited mobile devices. Methods are described that enable DRL optimization at the algorithm-, architecture-, and circuit-levels of abstraction.
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks...
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...
The applications of rapidly advancing intelligent systems are so varied that many are still yet to be discovered. There is often a disconnect between experts in computer science, artificial...
Bachelor Thesis from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 1.0, University of Duisburg-Essen, language: English, abstract: Reinforcement learning...
Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire...
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