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Fundamentals of Machine Learning$
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Thomas P. Trappenberg

Print publication date: 2019

Print ISBN-13: 9780198828044

Published to Oxford Scholarship Online: January 2020

DOI: 10.1093/oso/9780198828044.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 20 September 2021

Reinforcement learning

Reinforcement learning

Chapter:
(p.206) 10 Reinforcement learning
Source:
Fundamentals of Machine Learning
Author(s):

Thomas P. Trappenberg

Publisher:
Oxford University Press
DOI:10.1093/oso/9780198828044.003.0010

The discussion here considers a much more common learning condition where an agent, such as a human or a robot, has to learn to make decisions in the environment from simple feedback. Such feedback is provided only after periods of actions in the form of reward or punishment without detailing which of the actions has contributed to the outcome. This type of learning scenario is called reinforcement learning. This learning problem is formalized in a Markov decision-making process with a variety of related algorithms. The second part of this chapter will use function approximators with neural networks which have made recent progress as deep reinforcement learning.

Keywords:   reinforcement learning, Markov decision process, model-free reinforcement learning, model-based reinforcement learning, temporal difference learning, actor-critic

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