This is the code repository for Hands-On Q-Learning with Python, published by Packt.
Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
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 covers the following exciting features: Explore the fundamentals of reinforcement learning and the state-action-reward process Understand Markov decision processes Get well versed with libraries such as Keras, and TensorFlow Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym Choose and optimize a Q-Network’s learning parameters and fine-tune its performance Discover real-world applications and use cases of Q-learning
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All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
import gym
import numpy as np
env = gym.make('Taxi-v2')
state = env.reset()
Following is what you need for this book: If you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.
With the following software and hardware list you can run all code files present in the book (Chapter 1-8).
| Chapter | Software required |
|---|---|
| 1-8 | Python 3.5+, OpenAI Gym, Keras, TensorFlow(tentative) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Nazia Habib is a data scientist who has worked in a variety of industries to generate predictive analytics solutions for diverse groups of stakeholders. She is an expert in building solutions to optimization problems under conditions of uncertainty. Her projects range from predicting user behavior and engagement with social media apps to designing adaptive testing software. Her ongoing specialization is in designing custom reinforcement learning algorithms for modeling control problems with limited inputs that converge to optimal solutions.
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