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Hands-On Q-Learning with Python

You're reading from  Hands-On Q-Learning with Python

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781789345803
Pages 212 pages
Edition 1st Edition
Languages
Author (1):
Nazia Habib Nazia Habib
Profile icon Nazia Habib
Toc

Table of Contents (14) Chapters close

Preface 1. Section 1: Q-Learning: A Roadmap
2. Brushing Up on Reinforcement Learning Concepts 3. Getting Started with the Q-Learning Algorithm 4. Setting Up Your First Environment with OpenAI Gym 5. Teaching a Smartcab to Drive Using Q-Learning 6. Section 2: Building and Optimizing Q-Learning Agents
7. Building Q-Networks with TensorFlow 8. Digging Deeper into Deep Q-Networks with Keras and TensorFlow 9. Section 3: Advanced Q-Learning Challenges with Keras, TensorFlow, and OpenAI Gym
10. Decoupling Exploration and Exploitation in Multi-Armed Bandits 11. Further Q-Learning Research and Future Projects 12. Assessments 13. Other Books You May Enjoy

Google's DeepMind and the future of Q-learning

DeepMind Technologies is an artificial intelligence research firm established in 2010 and is currently a subsidiary of Alphabet Inc., the parent company of Google.

One of DeepMind's most famous successes is AlphaGo, the RL Go-playing machine that beat the current world champion and became the subject of a documentary. Another DeepMind RL agent, AlphaZero, taught itself to play and win not only Go, but also chess and shogi.

All of these machines, like the algorithms we've designed, used RL algorithms with deep learning architectures and taught themselves to play by starting with trial and error, then learning to model the games they were playing as state-action functions.

In December 2013, the DeepMind team developed and introduced deep Q-learning as an algorithm for solving optimization problems with better-than-human...

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