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Conference papers

Multiagent Planning and Learning As MILP

Jilles Dibangoye 1 Olivier Buffet 2 Akshat Kumar 3
1 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
2 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : The decentralized partially observable Markov decisionprocess offers a unified framework for sequential decision-making by multiple collaborating agents but remains in-tractable. Mixed-integer linear formulations proved use-ful for partially observable domains, unfortunately ex-isting applications restrict to domains with one or twoagents. In this paper, we exploit a linearization propertythat allows us to reformulate nonlinear constraints fromn-agent settings into linear ones. We further present plan-ning and learning approaches relying on MILP formula-tions for general and special cases, including network-distributed and transition-independent problems. Experi-ments on standard2-agent benchmarks as well as domainswith a large number of agents provide strong empiricalsupport to the methodology.
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Submitted on : Friday, December 18, 2020 - 11:13:55 AM
Last modification on : Wednesday, November 3, 2021 - 7:09:13 AM
Long-term archiving on: : Friday, March 19, 2021 - 8:35:51 PM


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  • HAL Id : hal-03081548, version 1


Jilles Dibangoye, Olivier Buffet, Akshat Kumar. Multiagent Planning and Learning As MILP. JFPDA 2020 - Journées Francophones surla Planification, la Décision et l’Apprentissagepour la conduite de systèmes, Jun 2020, Angers (virtuel), France. pp.1-12. ⟨hal-03081548⟩



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