Documents disponibles écrits par cet auteur

Learning agents for the multi-mode project scheduling problem / Wauters, T. in Journal of the operational research society (JORS), Vol. 62 N° 2 Special issue (Fevrier 2011) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 62 N° 2 Special issue (Fevrier 2011) . - pp. 281–290 Titre : Learning agents for the multi-mode project scheduling problem Type de document : texte imprimé Auteurs : Wauters, T., Auteur ; Verbeeck, K., Auteur ; Vanden Berghe, G., Auteur Année de publication : 2011 Article en page(s) : pp. 281–290 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Project  scheduling  Multi-agent  reinforcement  learning  Learning  automata Index. décimale : 001.424 Résumé : Intelligent optimization refers to the promising technique of integrating learning mechanisms into (meta-)heuristic search. In this paper, we use multi-agent reinforcement learning for building high-quality solutions for the multi-mode resource-constrained project scheduling problem (MRCPSP). We use a network of distributed reinforcement learning agents that cooperate to jointly learn a well-performing constructive heuristic. Each agent, being responsible for one activity, uses two simple learning devices, called learning automata, that learn to select a successor activity order and a mode, respectively. By coupling the reward signals for both learning tasks, we can clearly show the advantage of using reinforcement learning in search. We present some comparative results, to show that our method can compete with the best performing algorithms for the MRCPSP, yet using only simple learning schemes without the burden of complex fine-tuning. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n2/abs/jors2010101a.html [article] Learning agents for the multi-mode project scheduling problem [texte imprimé] / Wauters, T., Auteur ; Verbeeck, K., Auteur ; Vanden Berghe, G., Auteur . - 2011 . - pp. 281–290. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 62 N° 2 Special issue (Fevrier 2011) . - pp. 281–290 Mots-clés : Project  scheduling  Multi-agent  reinforcement  learning  Learning  automata Index. décimale : 001.424 Résumé : Intelligent optimization refers to the promising technique of integrating learning mechanisms into (meta-)heuristic search. In this paper, we use multi-agent reinforcement learning for building high-quality solutions for the multi-mode resource-constrained project scheduling problem (MRCPSP). We use a network of distributed reinforcement learning agents that cooperate to jointly learn a well-performing constructive heuristic. Each agent, being responsible for one activity, uses two simple learning devices, called learning automata, that learn to select a successor activity order and a mode, respectively. By coupling the reward signals for both learning tasks, we can clearly show the advantage of using reinforcement learning in search. We present some comparative results, to show that our method can compete with the best performing algorithms for the MRCPSP, yet using only simple learning schemes without the burden of complex fine-tuning. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n2/abs/jors2010101a.html