Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)
Study practical deep reinforcement learning using Q-Networks
Explore state-based unsupervised learning for machine learning models
Description
Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers.
This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you become familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in scientific research. Toward the end, you’ll gain insight into what’s in store for reinforcement learning.
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 libraries 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
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.
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