Journal Article10.1016/J.EJOR.2019.10.049
Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach
Yu Liu,Yiming Chen,Tao Jiang +2 more
152
TL;DR: A new selective maintenance optimization for multi-state systems that can execute multiple consecutive missions over a finite horizon is developed and a customized deep reinforcement learning method is put forth to overcome the “curse of dimensionality” and mitigate the uncountable state space.
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About: This article is published in European Journal of Operational Research. The article was published on 16 May 2020. The article focuses on the topics: Optimal maintenance & Maintenance actions.
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Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.
David Silver,Thomas Hubert,Julian Schrittwieser,Ioannis Antonoglou,Matthew Lai,Arthur Guez,Marc Lanctot,Laurent Sifre,Dharshan Kumaran,Thore Graepel,Timothy P. Lillicrap,Karen Simonyan,Demis Hassabis +12 more
TL;DR: This paper generalizes the AlphaZero approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games, and convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.
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