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2017 21st International Conference on System Theory, Control and Computing, ICSTCC 2017(1)
2018 7th International Conference on Systems and Control, ICSC 2018(1)
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A linear programming methodology for approximate dynamic programming
ArticleAbstract: The linear programming (LP) approach to solve the Bellman equation in dynamic programming is a well-Palabras claves:approximate dynamic programming, Control Applications, linear programming, Neural networksAutores:Armesto L., Henry Díaz, Sala A.Fuentes:scopusApproximate dynamic programming methodology for data-based optimal controllers
ArticleAbstract: In this article, we present a methodology for learning data-based approximately optimal controllers,Palabras claves:approximate dynamic programming, Intelligent Control, Neural Learning, Optimal ControlAutores:Armesto L., Henry Díaz, Sala A.Fuentes:scopusFitted Q-Function Control Methodology Based on Takagi-Sugeno Systems
ArticleAbstract: This paper presents a combined identification/Q-function fitting methodology that involves identificPalabras claves:Adaptive dynamic programing (DP), fitted Q-function, Linear Matrix Inequality (LMI), reinforcement learning (RL), Takagi-Sugeno (TS)Autores:Armesto L., Henry Díaz, Sala A.Fuentes:scopusImprovement of LMI controllers of Takagi-Sugeno models via Q-learning
Conference ObjectAbstract: This paper presents a preliminary attempt to bridge the conservative (shape-independent) results froPalabras claves:adaptive dynamic programming, LMI, Q-Learning, reinforcement learning, Takagi-SugenoAutores:Armesto L., Henry Díaz, Sala A.Fuentes:scopusImproving LMI controllers for discrete nonlinear systems using policy iteration
Conference ObjectAbstract: This paper presents a method to improve the conservative (shape-independent) controllers from guaranPalabras claves:LMI, policy iteration (pI), Q-Learning, reinforcement learning, Takagi-SugenoAutores:Armesto L., Henry Díaz, Sala A.Fuentes:scopusLearning Upper-Level Policy using Importance Sampling-based Policy Search Method
Conference ObjectAbstract: Policy search methods are a successful approach to reinforcement learning. These allow to learn uppePalabras claves:Autores:Armesto L., Esparza A., Henry Díaz, Pastor J., Sala A.Fuentes:scopus