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Hands-On Reinforcement Learning with Python

You're reading from  Hands-On Reinforcement Learning with Python

Product type Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781788836524
Pages 318 pages
Edition 1st Edition
Languages
Author (1):
Sudharsan Ravichandiran Sudharsan Ravichandiran
Profile icon Sudharsan Ravichandiran

Table of Contents (16) Chapters

Preface Introduction to Reinforcement Learning Getting Started with OpenAI and TensorFlow The Markov Decision Process and Dynamic Programming Gaming with Monte Carlo Methods Temporal Difference Learning Multi-Armed Bandit Problem Deep Learning Fundamentals Atari Games with Deep Q Network Playing Doom with a Deep Recurrent Q Network The Asynchronous Advantage Actor Critic Network Policy Gradients and Optimization Capstone Project – Car Racing Using DQN Recent Advancements and Next Steps Assessments Other Books You May Enjoy

Summary

In this chapter, we have learned the basics of RL and also some key concepts. We learned different elements of RL and different types of RL environments. We also covered the various available RL platforms and also the applications of RL in various domains.

In the next chapter, Chapter 2, Getting Started with OpenAI and TensorFlow, we will learn the basics of and how to install OpenAI and TensorFlow, followed by simulating environments and teaching the agents to learn in the environment.

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Hands-On Reinforcement Learning with Python
Published in: Jun 2018 Publisher: Packt ISBN-13: 9781788836524
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