<efrbr:recordSet xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:efrbr="http://vfrbr.info/efrbr/1.1" xmlns:efrbr-work="http://vfrbr.info/efrbr/1.1/work" xmlns:efrbr-expression="http://vfrbr.info/efrbr/1.1/expression" xmlns:efrbr-manifestation="http://vfrbr.info/efrbr/1.1/manifestation" xmlns:efrbr-person="http://vfrbr.info/efrbr/1.1/person" xmlns:efrbr-corporateBody="http://vfrbr.info/efrbr/1.1/corporateBody" xmlns:efrbr-concept="http://vfrbr.info/efrbr/1.1/concept" xmlns:efrbr-structure="http://vfrbr.info/efrbr/1.1/structure" xmlns:efrbr-responsible="http://vfrbr.info/efrbr/1.1/responsible" xmlns:efrbr-subject="http://vfrbr.info/efrbr/1.1/subject" xmlns:efrbr-other="http://vfrbr.info/efrbr/1.1/other" xsi:schemaLocation="http://vfrbr.info/efrbr/1.1 http://vfrbr.info/schemas/1.1/efrbr.xsd"><efrbr:entities><efrbr-work:work identifier="http://purl.tuc.gr/dl/dias/3CD2310F-9953-47D8-A9F7-CDE88C98BB55"><efrbr-work:titleOfTheWork>The performance impact of combining agent factorization with different learning algorithms for multiagent coordination</efrbr-work:titleOfTheWork></efrbr-work:work><efrbr-expression:expression identifier="http://purl.tuc.gr/dl/dias/3CD2310F-9953-47D8-A9F7-CDE88C98BB55"><efrbr-expression:titleOfTheExpression>The performance impact of combining agent factorization with different learning algorithms for multiagent coordination</efrbr-expression:titleOfTheExpression><efrbr-expression:formOfExpression vocabulary="DIAS:TYPES">
            Πλήρης Δημοσίευση σε Συνέδριο
            Conference Full Paper
         </efrbr-expression:formOfExpression><efrbr-expression:dateOfExpression type="issued">2024-09-13</efrbr-expression:dateOfExpression><efrbr-expression:dateOfExpression type="published">2022</efrbr-expression:dateOfExpression><efrbr-expression:languageOfExpression vocabulary="iso639-1">en</efrbr-expression:languageOfExpression><efrbr-expression:summarizationOfContent>Factorizing a multiagent system refers to partitioning the state-action space to individual agents and defining the interactions between those agents. This so-called agent factorization is of much importance in real-world industrial settings, and is a process that can have significant performance implications. In this work, we explore if the performance impact of agent factorization is different when using different learning algorithms in multiagent coordination settings. We evaluated six different agent factorization instances—or agent definitions—in the warehouse traffic management domain, comparing the performance of (mainly) two learning algorithms suitable for learning coordinated multiagent policies: the Evolutionary Strategies (ES), and a genetic algorithm (CCEA) previously used in this setting. Our results demonstrate that different learning algorithms are affected in different ways by alternative agent definitions. Given this, we can deduce that many important multiagent coordination problems can potentially be solved by an appropriate agent factorization in conjunction with an appropriate choice of a learning algorithm. Moreover, our work shows that ES is an effective learning algorithm for the warehouse traffic management domain; while, interestingly, celebrated policy gradient methods do not fare well in this complex real-world problem setting.</efrbr-expression:summarizationOfContent><efrbr-expression:useRestrictionsOnTheExpression type="creative-commons">http://creativecommons.org/licenses/by/4.0/</efrbr-expression:useRestrictionsOnTheExpression><efrbr-expression:note type="conference name">12th Hellenic Conference on Artificial Intelligence</efrbr-expression:note><efrbr-expression:note type="proceedings title">Proceedings of the 12th Hellenic Conference on Artificial Intelligence (SETN 2022)</efrbr-expression:note></efrbr-expression:expression><efrbr-person:person identifier="http://users.isc.tuc.gr/~akallinteris"><efrbr-person:nameOfPerson vocabulary="TUC:LDAP">
            Kallinteris Andreas
            Καλλιντερης Ανδρεας
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="http://users.isc.tuc.gr/~sorfanoudakis"><efrbr-person:nameOfPerson vocabulary="TUC:LDAP">
            Orfanoudakis Stavros
            Ορφανουδακης Σταυρος
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-person:person identifier="http://users.isc.tuc.gr/~gchalkiadakis"><efrbr-person:nameOfPerson vocabulary="TUC:LDAP">
            Chalkiadakis Georgios
            Χαλκιαδακης Γεωργιος
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-corporateBody:corporateBody identifier="https://v2.sherpa.ac.uk/id/publisher/21"><efrbr-corporateBody:nameOfTheCorporateBody vocabulary="S/R:PUBLISHERS">
            Association for Computing Machinery (ACM)
         </efrbr-corporateBody:nameOfTheCorporateBody></efrbr-corporateBody:corporateBody><efrbr-concept:concept identifier="1309BCB1-4319-4B41-99EE-D64E79F90BE2"><efrbr-concept:termForTheConcept>
            Agent factorization
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="D5804807-0496-46A9-9771-E0F253E3DA3E"><efrbr-concept:termForTheConcept>
            Multiagent coordination
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="E81EEB00-D5D0-4B5F-9FCD-DB2AA288E437"><efrbr-concept:termForTheConcept>
            Warehouse traffic management
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="63A62C49-8747-41FD-85D1-886575911D48"><efrbr-concept:termForTheConcept>
            Evolutionary strategies
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