Min Max Generalization for Deterministic Batch Mode Reinforcement Learning: Relaxation Schemes

DOI10.1137/120867263zbMath1284.49012OpenAlexW2057088773MaRDI QIDQ2873838

Raphael Fonteneau, Quentin Louveaux, Damien Ernst, Bernard Boigelot

Publication date: 27 January 2014

Published in: SIAM Journal on Control and Optimization (Search for Journal in Brave)

Full work available at URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.711.5580

zbMATH Keywords

computational complexitynonconvex optimizationrelaxation schemesbatch mode reinforcement learningmin--max problem

Mathematics Subject Classification ID

Minimax problems in mathematical programming (90C47) Abstract computational complexity for mathematical programming problems (90C60) Nonconvex programming, global optimization (90C26) Learning and adaptive systems in artificial intelligence (68T05) Existence of solutions for minimax problems (49J35) Methods involving semicontinuity and convergence; relaxation (49J45)

Uses Software

• SeDuMi

This page was built for publication: Min Max Generalization for Deterministic Batch Mode Reinforcement Learning: Relaxation Schemes