Deep Reinforcement Learning
Frontiers of Artificial Intelligence
(Sprache: Englisch)
This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing...
Voraussichtlich lieferbar in 3 Tag(en)
versandkostenfrei
Buch (Kartoniert)
171.19 €
85 DeutschlandCard Punkte
sammeln
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenlose Rücksendung
- Ratenzahlung möglich
Produktdetails
Produktinformationen zu „Deep Reinforcement Learning “
Klappentext zu „Deep Reinforcement Learning “
This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code.This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds - deep learning and reinforcement learning - to tap the potential of 'advanced artificial intelligence' for creating real-world applications and game-winning algorithms.
Inhaltsverzeichnis zu „Deep Reinforcement Learning “
Introduction to Reinforcement Learning.- Mathematical and Algorithmic understanding of Reinforcement Learning.- Coding the Environment and MDP Solution.- Temporal Difference Learning, SARSA, and Q Learning.- Q Learning in Code.- Introduction to Deep Learning.- Implementation Resources.- Deep Q Network (DQN), Double DQN and Dueling DQN.- Double DQN in Code.- Policy-Based Reinforcement Learning Approaches.- Actor-Critic Models & the A3C.- A3C in Code.- Deterministic Policy Gradient and the DDPG.- DDPG in Code.
Autoren-Porträt von Mohit Sewak
Mr. Sewak has been the Lead Data Scientist/Analytics Architect for a number of important international AI/DL/ML software and industry solutions and has also been involved in providing solutions and research for a series of cognitive features for IBM Watson Commerce. He has 14 years of experience working as a solutions architect using technologies like TensorFlow, Torch, Caffe, Theano, Keras, Open AI, SpaCy, Gensim, NLTK, Watson, SPSS, Spark, H2O, Kafka, ES, and others. Bibliographische Angaben
- Autor: Mohit Sewak
- 2020, 1st ed. 2019, XVII, 203 Seiten, 98 farbige Abbildungen, Maße: 15,6 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 9811382875
- ISBN-13: 9789811382871
Sprache:
Englisch
Kommentar zu "Deep Reinforcement Learning"
0 Gebrauchte Artikel zu „Deep Reinforcement Learning“
| Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
|---|
Schreiben Sie einen Kommentar zu "Deep Reinforcement Learning".
Kommentar verfassen