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arXiv:1811.12560cs.LG

An Introduction to Deep Reinforcement Learning

Vincent Francois-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau

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Abstract

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.

arXiv comments
Journal reference
Foundations and Trends in Machine Learning: Vol. 11, No. 3-4, 2018
DOI
doi:10.1561/2200000071
Subjects
cs.LGMachine Learning cs.AIArtificial Intelligence stat.MLMachine Learning (Statistics)

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Feb 2019 to Feb 2019. Found in 1 HN submission from Apr 2019 to Apr 2019. Open submission discussions.

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    • Beyond those, my favorite resources are: - "An Introduction to Deep Reinforcement Learning" by Vincent François-Lavet et al (https://arxiv.org/pdf/1811.12560.pdf) - "A (Long) Peek into Reinforcement Learning" by Lilian Weng…

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