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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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 36; H. 2; S. 2762 - 2776
Hauptverfasser:	Adeoye, Adeyemi D., Bemporad, Alberto
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2025
Schlagworte:	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
Online-Zugang:	Volltext
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