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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

Summary

In this chapter, we discussed the model-based approach to RL and implemented one of the recent research architectures from DeepMind, which augments the model of the environment into the model-free agents. This model tries to join both model-free and model-based paths into one, to allow the agent to decide which knowledge to use.

In the upcoming chapter (which will be the last in the book), we'll take a look at a recent DeepMind breakthrough in the area of full-information games: the AlphaGo Zero algorithm.

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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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