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...

Celý popis

Podrobná bibliografie

Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 36; číslo 2; s. 2762 - 2776
Hlavní autoři:	Adeoye, Adeyemi D., Bemporad, Alberto
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.02.2025
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!