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Dettaglio pubblicazione

2023, IFAC-PapersOnLine, Pages -2037 (volume: 56)

Distributed MARL with Limited Sensing for Robot Navigation Problems (04b Atto di convegno in volume)

Menegatti Danilo, Giuseppi Alessandro, Pietrabissa Antonio

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.
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