You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook

Published inJan 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838826994

Edition2nd Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Deep Reinforcement Learning

Author (1)

Maxim Lapan

SAC

In the final section, we will check our environments on the latest state-of-the-art method, called SAC, which was proposed by a group of Berkeley researchers and introduced in the paper Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning, by Tuomas Taarnoja et. al. arXiv 1801.01290, published in 2018.

At the moment, it's considered to be one of the best methods for continuous control problems. The core idea of the method is closer to the DDPG method than to A2C policy gradients. The SAC method might have been more logically described in Chapter 17, Continuous Action Space. However, in this chapter, we have the chance to compare it directly with PPO's performance, which was considered to be the de facto standard in continuous control problems for a long time.

The central idea in the SAC method is entropy regularization, which adds a bonus reward at each timestamp that is proportional to the entropy of the policy at this timestamp. In mathematical...

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Deep Reinforcement Learning Hands-On. - Second Edition

Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994

Author (1)

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

SAC

Unlock this book and the full library FREE for 7 days

Author (1)

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