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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

Digging Deeper into Deep Q-Networks with Keras and TensorFlow

In this chapter, we're going to build a deep Q-network to solve the well-known CartPole (inverted pendulum) problem. We'll be working with the OpenAI Gym CartPole-v1 environment. We'll also use Keras with a TensorFlow backend to implement our deep Q-network architecture.

We'll become familiar with OpenAI Gym's CartPole-v1 task and design a basic Deep Q Learning (DQN) structure. We'll construct our deep learning architecture using Keras and start to tune the learning parameters and add in epsilon decay to optimize the model. We'll also add in experience replay to improve our performance. At each iteration of our model-building process, we'll run a new training loop to observe the updated results.

The following topics will be covered in this chapter:

  • Getting started with the CartPole...

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