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Conference Papers Year : 2023

Collaborative Robot Learning For Indoor Environment

Abstract

During the Covid-19 pandemic, hospitals faced challenges in coping with the virus’s rapid spread. Collaborative multi-robot systems can help make hospital services more efficient and reduce human interactions. This study describes a multi-agent reinforcement learning (MARL) approach for the deployment of mobile robots in a hospital setting, with an emphasis on adaptability to dynamic environments. The protocol includes an exploration phase, Q-learning algorithm implementation, different MARL architectures (centralized, decentralized, and independent learning), and a collaborative learning aspect using a consensus algorithm.
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Dates and versions

hal-04398572 , version 1 (16-01-2024)

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Lucas Foissey, El-Hacen Diallo, Khaldoun Al Agha. Collaborative Robot Learning For Indoor Environment. 2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS), Jun 2023, Valencia, Spain. pp.203-210, ⟨10.1109/ICCNS58795.2023.10193717⟩. ⟨hal-04398572⟩
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