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Machine learning control

Machine learning control (MLC) is a subfield of machine learning, intelligent control, and control theory which aims to solve optimal control problems with machine learning methods. Key applications are complex nonlinear systems for which linear control theory methods are not applicable.

Applications, Types of problems and tasks & Adaptive Dynamic Programming

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Types of problems and tasks

Adaptive Dynamic Programming

Applications

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Machine learning control

Nodes25
Edges24
Triples6
Avg. degree1.92
Density0.08
Components1

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Machine learning control

Top relations

related to Adaptive Dynamic Programming · 5
Machine learning control → Adaptive Dynamic Programming, ADP, Hamilton-Jacobi-Bellman, HJB, The

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Important terminology

control mlc learning nonlinear problems programming optimal systems methods dynamic applications known function adp theory regression also law example general

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
neural networksinstance ofin traditional dynamic programming by approximating value functions or control policies using parametric structures0.80text
Machine learning controlrelated to Adaptive Dynamic ProgrammingAdaptive Dynamic Programming0.60section
Machine learning controlrelated to Adaptive Dynamic ProgrammingADP0.60section
Machine learning controlrelated to Adaptive Dynamic ProgrammingThe0.60section
Machine learning controlrelated to Adaptive Dynamic ProgrammingHamilton-Jacobi-Bellman0.60section
Machine learning controlrelated to Adaptive Dynamic ProgrammingHJB0.60section

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