Distributed systems can consist of thousands of network nodes interacting with each other. Given their size, managing these systems to perform optimally is a task that should be automated. Multi-agent reinforcement learning (MARL) is a suitable technique for tackling such problems. However, the application of MARL in a distributed system is not trivial. To bridge the gap between these two domains, we introduce the Marlon language. It enables MARL experts to focus on solving machine learning problems, rather than the complexities of distributed computing. We evaluate Marlon by comparing the implementation of a load balancing use case in Marlon with an ad-hoc implementation.
Original languageEnglish
Title of host publication34th ACM/SIGAPP Symposium On Applied Computing
Pages1322-1329
Number of pages8
DOIs
Publication statusPublished - 2019
EventThe 34th ACM/SIGAPP Symposium On Applied Computing (SAC 2019) - Limassol, Cyprus
Duration: 8 Apr 201912 Apr 2019
Conference number: 34
https://www.sigapp.org/sac/sac2019/

Publication series

NameProceedings of the ACM Symposium on Applied Computing
VolumePart F147772

Conference

ConferenceThe 34th ACM/SIGAPP Symposium On Applied Computing (SAC 2019)
Abbreviated titleSAC
CountryCyprus
CityLimassol
Period8/04/1912/04/19
Internet address

    Research areas

  • Distributed systems, Domain-specific languages, Multi-agent reinforcement learning

ID: 40531532