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TensorFlow Reinforcement Learning Quick Start Guide

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

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789533583
Length 184 pages
Edition 1st Edition
Languages
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Author (1):
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Kaushik Balakrishnan Kaushik Balakrishnan
Author Profile Icon Kaushik Balakrishnan
Kaushik Balakrishnan
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Toc

Table of Contents (11) Chapters Close

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

The A2C algorithm

The difference between A2C and A3C is that A2C performs synchronous updates. Here, all the workers will wait until they have completed the collection of experiences and computed the gradients. Only after this are the global (or master) network's parameters updated. This is different from A3C, where the update is performed asynchronously, that is, where the worker threads do not wait for the others to finish. A2C is easier to code than A3C, but that is not undertaken here. If you are interested in this, you are encouraged to take the preceding A3C code and convert it to A2C, after which the performance of both algorithms can be compared.

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