This is the code repository for Keras Reinforcement Learning Projects, published by Packt.
9 projects exploring popular reinforcement learning techniques to build self-learning agents
Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library.
This book covers the following exciting features: Practice the Markov decision process in prediction and betting evaluations Implement Monte Carlo methods to forecast environment behaviors Explore TD learning algorithms to manage warehouse operations Construct a Deep Q-Network using Python and Keras to control robot movements Apply reinforcement concepts to build a handwritten digit recognition model using an image dataset Address a game theory problem using Q-Learning and OpenAI Gym
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All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
plt.figure(figsize=(10,5))
plt.plot(dataset)
plt.show()
Following is what you need for this book: Keras Reinforcement Learning Projects is for you if you are data scientist, machine learning developer, or AI engineer who wants to understand the fundamentals of reinforcement learning by developing practical projects. Sound knowledge of machine learning and basic familiarity with Keras is useful to get the most out of this book
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 2-9 | Python 3.6 and above | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Giuseppe Ciaburro Giuseppe Ciaburro holds a PhD in environmental technical physics and two master's degrees. His research focuses on machine learning applications in the study of urban sound environments. He works at Built Environment Control Laboratory – Università degli Studi della Campania Luigi Vanvitelli (Italy). He has over 15 years of work experience in programming (in Python, R, and MATLAB), first in the field of combustion and then in acoustics and noise control. He has several publications to his credit.
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