An Inexact Sequential Quadratic Programming Method for Learning and Control of Recurrent Neural Networks

This article considers the two-stage approach to solving a partially observable Markov decision process (POMDP): the identification stage and the (optimal) control stage. We present an inexact sequential quadratic programming framework for recurrent neural network learning (iSQPRL) for solving the i...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 36; číslo 2; s. 2762 - 2776
Hlavní autori:	Adeoye, Adeyemi D., Bemporad, Alberto
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.02.2025
Predmet:	Gauss–Newton methods markov decision processes Neural networks numerical optimization Optimization Prediction algorithms Process control Quadratic programming Recurrent neural networks recurrent neural networks (RNNs) reinforcement learning (RL) sequential quadratic programming (SQP) Training
ISSN:	2162-237X, 2162-2388, 2162-2388
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