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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

Reinforcement Learning

Author (1):

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

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Summary

In this chapter, we saw how to apply RL algorithms to train an agent to learn to drive a car autonomously. We installed the TORCS racing-car simulator and also learned how to interface it with Python, so that we can train RL agents. We also did a deep dive into the state space for TORCS and the meaning of each of these terms. The DDPG algorithm was then used to train an agent to learn to drive successfully in TORCS. The video rendering in TORCS is really cool! The trained agent was able to drive more than seven to eight laps around the racetrack successfully. Finally, the use of PPO for the same problem of driving a car autonomously was also explored and left as an exercise for the interested reader; code for this is supplied in the book's repository.

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Authors (1)

Kaushik Balakrishnan

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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