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You're reading from  TensorFlow Reinforcement Learning Quick Start Guide

Product typeBook
Published inMar 2019
PublisherPackt
ISBN-139781789533583
Edition1st Edition
Tools
TensorFlow
Concepts
Reinforcement Learning
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Author (1)
Kaushik Balakrishnan
Kaushik Balakrishnan
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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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Summary

In this chapter, we introduced the A3C algorithm, which is an on-policy algorithm that's applicable to both discrete and continuous action problems. You saw how three different loss terms are combined into one and optimized. Python's threading library is useful for running multiple threads, with a copy of the policy network in each thread. These different workers compute the policy gradients and pass them on to the master to update the neural network parameters. We applied A3C to train agents for the CartPole and the LunarLander problems, and the agents learned them very well. A3C is a very robust algorithm and does not require a replay buffer, although it does require a local buffer for collecting a small number of experiences, after which it is used to update the networks. Lastly, a synchronous version of the algorithm, called A2C, was also introduced.

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TensorFlow Reinforcement Learning Quick Start Guide
Published in: Mar 2019Publisher: PacktISBN-13: 9781789533583
© 2019 Packt Publishing Limited All Rights Reserved

Author (1)

author image
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.
Read more about Kaushik Balakrishnan

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