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Reinforcement learning
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=== Deep reinforcement learning === This approach extends reinforcement learning by using a deep neural network and without explicitly designing the state space.<ref name="intro_deep_RL">{{cite journal |first= Vincent|display-authors=etal|last= Francois-Lavet |year=2018 |title= An Introduction to Deep Reinforcement Learning |journal=Foundations and Trends in Machine Learning|volume=11 |issue=3β4 |pages=219β354 |doi=10.1561/2200000071|arxiv= 1811.12560 |bibcode=2018arXiv181112560F|s2cid=54434537}}</ref> The work on learning ATARI games by Google [[DeepMind]] increased attention to [[deep reinforcement learning]] or [[end-to-end reinforcement learning]].<ref name="DQN2">{{cite journal |first= Volodymyr|display-authors=etal|last= Mnih |year=2015 |title= Human-level control through deep reinforcement learning |journal=Nature|volume=518 |issue=7540 |pages=529β533 |doi=10.1038/nature14236|pmid= 25719670 |bibcode=2015Natur.518..529M |s2cid=205242740}}</ref>
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