Deep Reinforcement Learning State of the art
(Sprache: Englisch)
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...
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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.
Autoren-Porträt von Youssef Fenjiro
Fenjiro, YoussefFenjiro Youssef, born in Morocco in 1978. He received the Master of degree in Computer Science from INPL, France, in 2001. In 2002, he joined Maroc Telecom where he currently holds the position of Project Manager Officer (PMP certified). He is also a data scientist (IBM certified) and Ph.d student in AI at Mohamed V University.
Bibliographische Angaben
- Autor: Youssef Fenjiro
- 2019, 64 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: Scholar's Press
- ISBN-10: 6138778707
- ISBN-13: 9786138778707
Sprache:
Englisch
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