Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
Language: English
Published by Packt Publishing, 2019
- Softcover
- New
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- Title
- Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
- Author
- Nazia Habib
- Publisher
- Packt Publishing
- Publication year
- 2019
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1789345804
- ISBN 13
- 9781789345803
- Item weight
- 494 grams
Key Features
- Use Q-learning to train deep learning models using Markov decision processes (MDPs)
- Study practical deep reinforcement learning using deep Q-networks
- Explore state-based unsupervised learning for machine learning models
This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as frameworks such as Keras and TensorFlow.
By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.
What you will learn
- Explore the fundamentals of reinforcement learning and the state-action-reward process
- Understand Markov decision processes
- Get well versed with frameworks such as Keras and TensorFlow
- Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym
- Choose and optimize a Q-Network's learning parameters and fine-tune its performance
- Discover real-world applications and use cases of Q-learning
Table of Contents
- Brushing Up on Reinforcement Learning Concepts
- Getting Started with the Q-Learning Algorithm
- Setting Up Your First Environment with OpenAI Gym
- Teaching a Smartcab to Drive Using Q-Learning
- Building Q-Networks with TensorFlow
- Digging Deeper into Deep Q-Networks with Keras and TensorFlow
- Decoupling Exploration and Exploitation in Multi-Armed Bandits
- Further Q-Learning Research and Future Projects
- Assessments
"Synopsis" may belong to another edition of this title.
About the Author
Nazia Habib is a data scientist who has worked in a variety of industries to generate predictive analytics solutions for diverse groups of stakeholders. She is an expert in building solutions to optimization problems under conditions of uncertainty. Her projects range from predicting user behavior and engagement with social media apps to designing adaptive testing software. Her ongoing specialization is in designing custom reinforcement learning algorithms for modeling control problems with limited inputs that converge to optimal solutions.
"About the title" may belong to another edition of this title.
Ria Christie Collections
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