This is the code repository for TensorFlow Reinforcement Learning Quick Start Guide, published by Packt.
Get up and running with training and deploying intelligent, self-learning agents using Python
Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving.
This book covers the following exciting features:
- Understand the theory and concepts behind modern Reinforcement Learning algorithms
- Code state-of-the-art Reinforcement Learning algorithms with discrete or continuous actions
- Develop Reinforcement Learning algorithms and apply them to training agents to play computer games
- Explore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlow
- Use A3C to play CartPole and LunarLander
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:
import numpy as np
import sys
import matplotlib.pyplot as plt
Following is what you need for this book: Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.
With the following software and hardware list you can run all code files present in the book (Chapter 1-08).
| Chapter | Software required | OS required |
|---|---|---|
| 1-08 | Python, TensorFlow | 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.
Kaushik Balakrishnan works for BMW in Silicon Valley, and applies reinforcement learning, machine learning, and computer vision to solve problems in autonomous driving. Previously, he also worked at Ford Motor Company and NASA Jet Propulsion Laboratory. His primary expertise is in machine learning, computer vision, and high-performance computing, and he has worked on several projects involving both research and industrial applications. He has also worked on numerical simulations of rocket landings on planetary surfaces, and for this he developed several high-fidelity models that run efficiently on supercomputers. He holds a PhD in aerospace engineering from the Georgia Institute of Technology in Atlanta, Georgia.
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