You're reading from TensorFlow Reinforcement Learning Quick Start Guide Get up and running with training and deploying intelligent, self-learning agents using Python

Product type Paperback

Published in Mar 2019

Publisher Packt

ISBN-13 9781789533583

Length 184 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Reinforcement Learning

Author (1):

Kaushik Balakrishnan

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Table of Contents (11) Chapters

Preface

1. Up and Running with Reinforcement Learning FREE CHAPTER

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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Questions

Is A3C an on-policy or off-policy algorithm?
Why is the Shannon entropy term used?
What are the problems with using a large number of worker threads?
Why is softmax used in the policy neural network?
Why do we need an advantage function?
This is left as an exercise: For the LunarLander problem, repeat the training without reward shaping and see if the agent learns faster/slower than what we saw in this chapter.

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Authors (1)

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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