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TensorFlow Reinforcement Learning Quick Start Guide

You're reading from  TensorFlow Reinforcement Learning Quick Start Guide

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789533583
Pages 184 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Kaushik Balakrishnan Kaushik Balakrishnan
Profile icon Kaushik Balakrishnan
LinkedIn
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.
See other products by Kaushik Balakrishnan

Table of Contents (11) Chapters

Preface 1. Up and Running with Reinforcement Learning 2. Temporal Difference, SARSA, and Q-Learning 3. Deep Q-Network 4. Double DQN, Dueling Architectures, and Rainbow 5. Deep Deterministic Policy Gradient 6. Asynchronous Methods - A3C and A2C 7. Trust Region Policy Optimization and Proximal Policy Optimization 8. Deep RL Applied to Autonomous Driving 9. Assessment 10. Other Books You May Enjoy

Training a PPO agent

We saw previously how to train a DDPG agent to drive a car on TORCS. How to use a PPO agent is left as an exercise for the interested reader. This is a nice challenge to complete. The PPO code from Chapter 7, Trust Region Policy Optimization and Proximal Policy Optimization, can be reused, with the necessary changes made to the TORCS environment. The PPO code for TORCS is also supplied in the code repository (https://github.com/PacktPublishing/TensorFlow-Reinforcement-Learning-Quick-Start-Guide), and the interested reader can peruse it. A cool video of a PPO agent driving a car in TORCS is in the following YouTube video at: https://youtu.be/uE8QaJQ7zDI

Another challenge for the interested reader is to use Trust Region Policy Optimization (TRPO) for the TORCS racing car problem. Try this too, if interested! This is one way to master RL algorithms.

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Authors (1)

Profile icon Kaushik Balakrishnan
LinkedIn
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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See other products by Kaushik Balakrishnan

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