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Deep Reinforcement Learning Hands-On

You're reading from  Deep Reinforcement Learning Hands-On

Product type Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781788834247
Pages 546 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Maxim Lapan Maxim Lapan
Profile icon Maxim Lapan
LinkedIn
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.
See other products by Maxim Lapan

Table of Contents (23) Chapters

Deep Reinforcement Learning Hands-On
Contributors
Preface
Other Books You May Enjoy
What is Reinforcement Learning? OpenAI Gym Deep Learning with PyTorch The Cross-Entropy Method Tabular Learning and the Bellman Equation Deep Q-Networks DQN Extensions Stocks Trading Using RL Policy Gradients – An Alternative The Actor-Critic Method Asynchronous Advantage Actor-Critic Chatbots Training with RL Web Navigation Continuous Action Space Trust Regions – TRPO, PPO, and ACKTR Black-Box Optimization in RL Beyond Model-Free – Imagination AlphaGo Zero Index

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 the concrete examples later to support your intuition! Let's start with a deterministic case, when all our actions have a 100% guaranteed outcome. Imagine that our agent observes state and has N available actions. Every action leads to another state, , with a respective reward, . Also assume that we know the values, , of all states connected to the state . What will be the best course of action that the agent can take in such a state?

Figure 3: An abstract environment with N states reachable from the initial state

If we choose the concrete action , 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're using discount factor , we need to multiply the value of the next state...

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Authors (1)

Profile icon Maxim Lapan
LinkedIn
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.
See other products by Maxim Lapan

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