Deep Reinforcement Learning State of the art

Youssef Fenjiro

ISBN 10: 6138778707 / ISBN 13: 9786138778707

Editore: Scholars' Press, 2019

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nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial intelligence has made big steps forward with reinforcement learning (RL) in the last century, and with the advent of deep learning (DL) in the 90s, especially, the breakthrough of convolutional networks in computer vision field. The adoption of DL neural networks in RL, in the first decade of the 21 century, led to an end-to-end framework allowing a great advance in human-level agents and autonomous systems, called deep reinforcement learning (DRL). In this book, we will go through the development Timeline of RL and DL technologies, describing the main improvements made in both fields. Then, we will dive into DRL and have an overview of the state-of-the-art of this new and promising field, by browsing a set of algorithms (Value optimization, Policy optimization and Actor-Critic), then, giving an outline of current challenges and real-world applications, along with the hardware and frameworks used.

Codice articolo 9786138778707

Dati bibliografici

• Titolo: Deep Reinforcement Learning State of the art
• Editore: Scholars' Press
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 6138778707
• ISBN 13: 9786138778707
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 220 L x 150 A x 4 P (millimetri) 220 millimetri di larghezza per 150 millimetri di altezza per 4 millimetri di profondità
• Peso: 113 grammi

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Riassunto

Artificial intelligence has made big steps forward with reinforcement learning (RL) in the last century, and with the advent of deep learning (DL) in the 90s, especially, the breakthrough of convolutional networks in computer vision field. The adoption of DL neural networks in RL, in the first decade of the 21 century, led to an end-to-end framework allowing a great advance in human-level agents and autonomous systems, called deep reinforcement learning (DRL). In this book, we will go through the development Timeline of RL and DL technologies, describing the main improvements made in both fields. Then, we will dive into DRL and have an overview of the state-of-the-art of this new and promising field, by browsing a set of algorithms (Value optimization, Policy optimization and Actor-Critic), then, giving an outline of current challenges and real-world applications, along with the hardware and frameworks used.