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

Value, state, and optimality

You may remember our definition of the value of the state from Chapter 1, What Is Reinforcement Learning?. This is a very important notion and the time has come to explore it further.

This whole part of the book is built around the value and how to approximate it. We defined the value as an expected total reward (optionally discounted) that is obtainable from the state. In a formal way, the value of the state is , where r_t is the local reward obtained at step t of the episode.

The total reward could be discounted with or not (the undiscounted case corresponds to ); it's up to us how to define it. The value is always calculated in terms of some policy that our agent follows. To illustrate this, let's consider a very simple environment with three states:

The agent's initial state.
The final state that the agent is in after executing action "right" from the initial state. The reward obtained from this is 1.

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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

Value, state, and optimality

Unlock this book and the full library FREE for 7 days

Author (1)

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