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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

OpenAI Gym and RL research

As we discussed in the first chapter, OpenAI Gym is an attempt to standardize reinforcement learning research and development and to compare RL models to each other for the purposes of developing baseline research frameworks.

The following screenshot is a still from the Neon Race Car environment with OpenAI Gym and Universe:

As RL researchers, we want to be able to develop benchmarks and widely-used, well-known training and testing datasets like the ones available for supervised learning, such as ImageNet for image recognition, the familiar iris dataset from the UCI Machine Learning Repository, or the MNIST handwritten digit dataset.

The RL analogue for a widely-used labeled training dataset is a standardized set of environments such as the one that Gym provides. A standardized set of environments lets us compare the work of different researchers,...

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