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

PPO

Historically, the PPO method came from the OpenAI team and it was proposed long after TRPO, which is from 2015. However, PPO is much simpler than TRPO, so we will start with it. The 2017 paper in which it was proposed is by John Schulman et. al., and it is called Proximal Policy Optimization Algorithms (arXiv:1707.06347).

The core improvement over the classic A2C method is changing the formula used to estimate the policy gradients. Instead of using the gradient of logarithm probability of the action taken, the PPO method uses a different objective: the ratio between the new and the old policy scaled by the advantages.

In math form, the old A2C objective could be written as . The new objective proposed by PPO is .

The reason behind changing the objective is the same as with the cross-entropy method covered in Chapter 4, The Cross-Entropy Method: importance sampling. However, if we just start to blindly maximize this value, it may lead to a very large update to the policy...

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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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