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This is the code repository for Mastering Reinforcement Learning with Python, published by Packt.
Build next-generation, self-learning models using reinforcement learning techniques and best practices
Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.
This book covers the following exciting features:
- Model and solve complex sequential decision-making problems using RL
- Develop a solid understanding of how state-of-the-art RL methods work
- Use Python and TensorFlow to code RL algorithms from scratch
- Parallelize and scale up your RL implementations using Ray's RLlib package
- Get in-depth knowledge of a wide variety of RL topics
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
ug = UserGenerator()
visualize_bandits(ug)
Following is what you need for this book: This book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.
With the following software and hardware list you can run all code files present in the book (Chapter 1-18).
| Chapter | Software required | OS required |
|---|---|---|
| 1-18 | PyCharm Or Visual Studio Code | Linux (recommended), macOS, Windows |
| 1-18 | Python 3.6+ | Linux (recommended), macOS, Windows) |
| 1-18 | TensorFlow 2.3 | Linux (recommended), macOS, Windows) |
| 1-18 | Ray/RLlib 1.0.1 | Linux (recommended), macOS, Windows |
| 1-18 | NumPy | Linux (recommended), macOS, Windows) |
| 1-18 | Pandas | Linux (recommended), macOS, Windows |
| 1-18 | SciPy | Linux (recommended), macOS, Windows |
| 1-18 | Scikit-learn 0.23.2 | Linux (recommended), macOS, Windows |
| 1-18 | Jupyter Notebook | Linux (recommended), macOS, Windows |
| 1-18 | Plotly 4.10.0 | Linux (recommended), macOS, Windows) |
| 1-18 | Cufflinks 0.17.3 | Linux (recommended), macOS, Windows |
| 1-18 | Plotly 4.10.0 Linux (recommended), macOS, Windows | |
| 1-18 | Gym 0.15+ | Linux (recommended), macOS, Windows |
| 1-18 | PyBullet | Linux (recommended), macOS, Windows |
| 1-18 | TensorTrade | Linux (recommended), macOS, Windows |
| 1-18 | Flow | Linux (recommended), macOS |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.
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