Meshal Alharbi — accepted AI-conference papers

2026

GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding

Johannes Gaber, Meshal Alharbi, Daniele Gammelli, Gioele Zardini

RA-L 2026

Large robot fleets are now common in warehouses and other logistics settings, where small control gains translate into large operational impacts. In this article, we address task scheduling for lifelong Multi-Agent Pickup-and-Delivery (MAPD) and propose a hybrid method that couples learning-based gl

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2024

Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge

Meshal Alharbi, Mardavij Roozbehani, Munther Dahleh

AAAI 2024technical

The problem of sample complexity of online reinforcement learning is often studied in the literature without taking into account any partial knowledge about the system dynamics that could potentially accelerate the learning process. In this paper, we study the sample complexity of online Q-learning…

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