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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

Introducing CartPole-v1

Your task in the CartPole environment is simple: move a cart back and forth along a wire so that a pole pivoting on the cart balances upright. In control theory, this is called the inverted pendulum problem, and it is one of several classic control theory problems implemented as reinforcement learning environments in OpenAI Gym.

Here's an illustration of the Gym implementation of the task:

The inverted pendulum as defined in control theory is an underactuated system, meaning it has more degrees of freedom than actuated (controllable) types of movement.

In other words, the position of the cart can be directly controlled, but the movement of the pole cannot. However, the pole can move freely around the joint and the cart can move back and forth. This makes one actuated source of movement and two degrees of freedom.

The inverted pendulum system is unstable...

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