Deep Reinforcement Learning

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Grokking Deep Reinforcement Learning  eBooks & eLearning

Posted by IrGens at Sept. 26, 2021
Grokking Deep Reinforcement Learning

Grokking Deep Reinforcement Learning by Miguel Morales
English | November 10, 2020 | ISBN: 1617295450 | True EPUB | 472 pages | 20.96 MB
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Deep Reinforcement Learning in Action, Video Edition  eBooks & eLearning

Posted by IrGens at Dec. 7, 2023
Deep Reinforcement Learning in Action, Video Edition

Deep Reinforcement Learning in Action, Video Edition
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 11h 59m | 1.76 GB
Authors: Brandon Brown, Alexander Zai
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Deep Reinforcement Learning in Action  eBooks & eLearning

Posted by IrGens at Oct. 7, 2021
Deep Reinforcement Learning in Action

Deep Reinforcement Learning in Action by Brandon Brown, Alexander Zai
English | April 28, 2020 | ISBN: 1617295434 | True EPUB | 325 pages | 8.85 MB
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Deep Reinforcement Learning for Wireless Networks  eBooks & eLearning

Posted by AvaxGenius at Sept. 18, 2020
Deep Reinforcement Learning for Wireless Networks

Deep Reinforcement Learning for Wireless Networks by F. Richard Yu
English | PDF(Reospt),EPUB | 2019 | 78 Pages | ISBN : 3030105458 | 10.3 MB

This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme.
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Deep Reinforcement Learning: Fundamentals, Research and Applications  eBooks & eLearning

Posted by roxul at June 29, 2020
Deep Reinforcement Learning: Fundamentals, Research and Applications

Hao Dong, "Deep Reinforcement Learning: Fundamentals, Research and Applications"
English | ISBN: 9811540942 | 2020 | 541 pages | EPUB, PDF | 44 MB + 11 MB
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State-of-the-art Research of Deep Reinforcement-learning  eBooks & eLearning

Posted by BlackDove at June 22, 2022
State-of-the-art Research of Deep Reinforcement-learning

State-of-the-art Research of Deep Reinforcement-learning
Published 06/2022
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 679 MB | Duration: 20 lectures • 30m


OpenAI research, DeepMind research, Google research, Microsoft research
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Deep Reinforcement Learning: A Hands-on Tutorial in Python  eBooks & eLearning

Posted by Sigha at Oct. 14, 2019
Deep Reinforcement Learning: A Hands-on Tutorial in Python

Deep Reinforcement Learning: A Hands-on Tutorial in Python
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 48000 Hz, 2ch | 1.48 GB
Duration: 4 hours | Genre: eLearning | Language: English

Learn the highly in-demand skill of Reinforcement Learning in a simple and practical way using Python and Keras.
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Fast Deep Reinforcement Learning  eBooks & eLearning

Posted by BlackDove at Aug. 14, 2022
Fast Deep Reinforcement Learning

Fast Deep Reinforcement Learning
Published 08/2022
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 842 MB | Duration: 30 lectures • 2h 26m


Learn to Build Powerful Deep RL Agents using Ray RLlib and OpenAI Gym
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Deep Reinforcement Learning with Python, 2nd Edition [Repost]  eBooks & eLearning

Posted by IrGens at April 25, 2021
Deep Reinforcement Learning with Python, 2nd Edition [Repost]

Deep Reinforcement Learning with Python: Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow, 2nd Edition by Sudharsan Ravichandiran
English | September 30, 2020 | ISBN: 1839210680 | True EPUB/PDF | 760 pages | 27.8/27.3 MB
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Reinforcement Learning: Aktuelle Ansätze verstehen - mit Beispielen in Java und Greenfoot  eBooks & eLearning

Posted by AvaxGenius at Sept. 12, 2020
Reinforcement Learning: Aktuelle Ansätze verstehen - mit Beispielen in Java und Greenfoot

Reinforcement Learning: Aktuelle Ansätze verstehen - mit Beispielen in Java und Greenfoot by Uwe Lorenz
Deutsch | EPUB | 2020 | 184 Pages | ISBN : 3662616505 | 12.8 MB

In uralten Spielen wie Schach oder Go können sich die brillantesten Spieler verbessern, indem sie die von einer Maschine produzierten Strategien studieren. Robotische Systeme üben ihre Bewegungen selbst. In Arcade Games erreichen lernfähige Agenten innerhalb weniger Stunden übermenschliches Niveau. Wie funktionieren diese spektakulären Algorithmen des bestärkenden Lernens?
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