Tag: Reinforcement

Mastering Reinforcement Learning with Python


Free Download Enes Bilgin, "Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices"
English | 2020 | pages: 532 | ISBN: 1838644148 | PDF | 14,1 mb
Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices

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Applied Reinforcement Learning with Python With OpenAI Gym, Tensorflow, and Keras


Free Download Applied Reinforcement Learning with Python: With OpenAI Gym, Tensorflow, and Keras by Taweh Beysolow II
English | August 24, 2019 | ISBN: 1484251261 | 183 pages | MOBI | 4.61 Mb
Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym.

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Reinforcement Learning for Finance Solve Problems in Finance with CNN and RNN Using the TensorFlow Library


Free Download Reinforcement Learning for Finance: Solve Problems in Finance with CNN and RNN Using the TensorFlow Library by Samit Ahlawat
English | December 27, 2022 | ISBN: 1484288343 | 438 pages | MOBI | 16 Mb
This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library.

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Coursera – Reinforcement Learning Specialization


Free Download Coursera – Reinforcement Learning Specialization
Last updated 6/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 13h 18m | Size: 4.52 GB
Master the Concepts of Reinforcement Learning. Implement a complete RL solution and understand how to apply AI tools to solve real-world problems.

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Reinforcement Learning for Sequential Decision and Optimal Control


Free Download Reinforcement Learning for Sequential Decision and Optimal Control
English | 2023 | ISBN: 9811977836 | 484 Pages | PDF (True) | 23 MB
Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, including chess games, computer games and robotic control. RL is also considered to be a promising and powerful tool to create general artificial intelligence in the future.

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