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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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Chapter 7

Trust Region Policy Optimization (TRPO) has an objective function and a constraint. It hence requires a second order optimization such as a conjugate gradient. SGD and Adam are not applicable in TRPO.
The entropy term helps in regularization. It allows the agent to explore more.
We clip the policy ratio to limit the amount by which one update step will change the policy. If this clipping parameter epsilon is large, the policy can change drastically in each update, which can result in a sub-optimal policy, as the agent's policy is noisier and has too many fluctuations.
The action is bounded between a negative and a positive value, and so the tanh activation function is used for mu. For sigma, the softplus is used as sigma and is always positive. The tanh function cannot be used for sigma, as tanh can result in negative values for sigma, which is meaningless!
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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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