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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

Demystifying MDPs

The technical purpose of Q-learning is to discover solutions for a type of optimization problem called an MDP.

When we talk about states and the actions that we can take from states, we are discussing concepts developed in the context of MDPs (and the Markov chains and other state space models that they are derived from).

Control processes

An MDP is itself a type of problem called a control process. Broadly speaking, a control process is designed to optimize a value or a set of values within a set of limitations. The following diagram shows a model for a generalized control process:

A real-world example of such an optimization process might be maximizing a company's profit by knowing its gross sales...

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