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

The Bellman equation of optimality

To explain the Bellman equation, it's better to go a bit abstract. Don't be afraid; I'll provide concrete examples later to support your learning! Let's start with a deterministic case, when all our actions have a 100% guaranteed outcome. Imagine that our agent observes state s₀ and has N available actions. Every action leads to another state, s₁ ... s_N, with a respective reward, r₁ ... r_N. Also, assume that we know the values, V_i, of all states connected to state s₀. What will be the best course of action that the agent can take in such a state?

Figure 5.3: An abstract environment with N states reachable from the initial state

If we choose the concrete action, a_i, and calculate the value given to this action, then the value will be . So, to choose the best possible action, the agent needs to calculate the resulting values for every action and choose the maximum possible outcome. In other words, . If we are using the...

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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

The Bellman equation of optimality

Unlock this book and the full library FREE for 7 days

Author (1)

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