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Hands-On Q-Learning with Python

Hands-On Q-Learning with Python

By : Nazia Habib
2.3 (3)
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Hands-On Q-Learning with Python

Hands-On Q-Learning with Python

2.3 (3)
By: Nazia Habib

Overview of this book

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.
Table of Contents (14 chapters)
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1
Section 1: Q-Learning: A Roadmap
6
Section 2: Building and Optimizing Q-Learning Agents
9
Section 3: Advanced Q-Learning Challenges with Keras, TensorFlow, and OpenAI Gym

A brief overview of neural networks

Broadly speaking, a neural network is a type of machine learning framework that is built for pattern-matching. Neural networks are often used to classify input data, such as images or text, based on the extensional definitions of the type of object they are identifying. A classifier network, for example, might be given images as input and labels as output, and then use this to determine an internal function that will map the Inputs to the Outputs:

The first black box operation in the preceding diagram indicates that the network is being trained and is updating its approximation of the input and output function based on incoming data. The second box represents the testing process, where the network uses its internal function to make predictions on new incoming data.

Think of the way a machine learning algorithm, such as a decision tree classifier...

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