June 5, 2024, 4:44 a.m. | Leopoldo Agorio, Sean Van Alen, Miguel Calvo-Fullana, Santiago Paternain, Juan Andres Bazerque

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.01782v1 Announce Type: cross
Abstract: We address the conflicting requirements of a multi-agent assignment problem through constrained reinforcement learning, emphasizing the inadequacy of standard regularization techniques for this purpose. Instead, we recur to a state augmentation approach in which the oscillation of dual variables is exploited by agents to alternate between tasks. In addition, we coordinate the actions of the multiple agents acting on their local states through these multipliers, which are gossiped through a communication network, eliminating the need …

abstract agent agents arxiv assignment augmentation cs.ai cs.lg cs.ma cs.sy eess.sy multi-agent problem regularization reinforcement reinforcement learning requirements standard state tasks through type variables via

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