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You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook
Published inJan 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781838826994
Edition2nd Edition
Languages
Python
Tools
KerasTensorFlow
Concepts
Deep Reinforcement Learning
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Author (1)
Maxim Lapan
Maxim Lapan
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Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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References

  1. Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver, 2017, Rainbow: Combining Improvements in Deep Reinforcement Learning. arXiv:1710.02298
  2. Sutton, R.S., 1988, Learning to Predict by the Methods of Temporal Differences, Machine Learning 3(1):9-44
  3. Hado Van Hasselt, Arthur Guez, David Silver, 2015, Deep Reinforcement Learning with Double Q-Learning. arXiv:1509.06461v3
  4. Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Pilot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg, 2017, Noisy Networks for Exploration. arXiv:1706.10295v1
  5. Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaus, David Saxton, Remi Munos, 2016, Unifying Count-Based Exploration and Intrinsic Motivation. arXiv:1606.01868v2
  6. Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, Marcus Hutter, 2017, Count-Based...
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Deep Reinforcement Learning Hands-On. - Second Edition
Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994
© 2020 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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