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Hands-On ROS for Robotics Programming

You're reading from   Hands-On ROS for Robotics Programming Program highly autonomous and AI-capable mobile robots powered by ROS

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781838551308
Length 432 pages
Edition 1st Edition
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Author (1):
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Bernardo Ronquillo Japón Bernardo Ronquillo Japón
Author Profile Icon Bernardo Ronquillo Japón
Bernardo Ronquillo Japón
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Physical Robot Assembly and Testing
2. Assembling the Robot FREE CHAPTER 3. Unit Testing of GoPiGo3 4. Getting Started with ROS 5. Section 2: Robot Simulation with Gazebo
6. Creating the Virtual Two-Wheeled ROS Robot 7. Simulating Robot Behavior with Gazebo 8. Section 3: Autonomous Navigation Using SLAM
9. Programming in ROS - Commands and Tools 10. Robot Control and Simulation 11. Virtual SLAM and Navigation Using Gazebo 12. SLAM for Robot Navigation 13. Section 4: Adaptive Robot Behavior Using Machine Learning
14. Applying Machine Learning in Robotics 15. Machine Learning with OpenAI Gym 16. Achieve a Goal through Reinforcement Learning 17. Assessment 18. Other Books You May Enjoy

Understanding the ROS Machine Learning packages

The code for this chapter implements the classical reinforcement learning methodology of training a neural network. This neural network is mathematically similar to the one we introduced in Chapter 10, Applying Machine Learning in Robotics, stacking layers of (hidden) nodes to establish a relationship between the states (the input layer) and the actions (the output layer).

The algorithm we will use for reinforcement learning is called Deep Q-Network (DQN) and was introduced in Chapter 11, Machine Learning with OpenAI Gym in the Running an environment section. In the next section, Setting the training task parameters, you will be given the operational description of states, actions, and rewards that characterize the reinforcement learning problem that we are going to solve with ROS.

Next, we will present the training scenarios, and...

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