This paper proposes a Multi-Agent Reinforcement Learning (MARL) algorithm for the multi-robot navigation problem. Most of the proposals in the literature requires some form of information sharing and communications among agents to coordinate their action in order to complete the overall task. The proposed paper, named Limited Sensing MARL (LS-MARL), assumes that each robot decisions rely on local information and is provided with sensor, which can be switched on for the localization of the robots within a given range. Besides the navigation task, each agent aims at limiting the use of the sensor as much as possible (i.e., to be as independent as possible) for energy saving or safety reasons. The algorithm is evaluated by simulations and favourably compares to the one proposed in (Yu et al. (2015)), that assumes a similar setup in which the neighbouring agents share their positioning information.

Distributed MARL with Limited Sensing for Robot Navigation Problems / Menegatti, Danilo; Giuseppi, Alessandro; Pietrabissa, Antonio. - 56:2(2023), pp. 2032-2037. (Intervento presentato al convegno 22nd World Congress of the International Federation of Automatic Control (IFAC) tenutosi a Yokohama; Japan) [10.1016/j.ifacol.2023.10.1100].

Distributed MARL with Limited Sensing for Robot Navigation Problems

Menegatti, Danilo
;
Giuseppi, Alessandro
;
Pietrabissa, Antonio
2023

Abstract

This paper proposes a Multi-Agent Reinforcement Learning (MARL) algorithm for the multi-robot navigation problem. Most of the proposals in the literature requires some form of information sharing and communications among agents to coordinate their action in order to complete the overall task. The proposed paper, named Limited Sensing MARL (LS-MARL), assumes that each robot decisions rely on local information and is provided with sensor, which can be switched on for the localization of the robots within a given range. Besides the navigation task, each agent aims at limiting the use of the sensor as much as possible (i.e., to be as independent as possible) for energy saving or safety reasons. The algorithm is evaluated by simulations and favourably compares to the one proposed in (Yu et al. (2015)), that assumes a similar setup in which the neighbouring agents share their positioning information.
2023
22nd World Congress of the International Federation of Automatic Control (IFAC)
multi-agent reinforcement learning; dynamic programming; distributed control; robot navigation; sparse interactions
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Distributed MARL with Limited Sensing for Robot Navigation Problems / Menegatti, Danilo; Giuseppi, Alessandro; Pietrabissa, Antonio. - 56:2(2023), pp. 2032-2037. (Intervento presentato al convegno 22nd World Congress of the International Federation of Automatic Control (IFAC) tenutosi a Yokohama; Japan) [10.1016/j.ifacol.2023.10.1100].
File allegati a questo prodotto
File Dimensione Formato  
Menegatti_Distributed_2023.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 865.66 kB
Formato Adobe PDF
865.66 kB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1692411
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact