Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2026

Abstract

Multi-agent reinforcement learning enables sophisticated collaborative behaviors in autonomous systems, yet fundamental scalability barriers persist: existing methods struggle to coordinate large agent populations and face challenges with extended decision-making horizons. This research develops hierarchical approaches to scale up multi-agent learning systems through two complementary directions: structural scaling for coordinating increasing numbers of agents and temporal scaling for extending decision-making horizons. This paper presents four integrated contributions: a taxonomic survey establishing hierarchical architectures as the theoretical foundation for scalable multi-agent learning systems, a benchmark for long-horizon multi-objective multi-agent reinforcement learning, a framework integrating self-organizing neural networks with multiple reinforcement learning agents for hierarchical tri-level control, and a framework leveraging large language models for zero-shot multi-agent planning. Through comprehensive validation, this work demonstrates that hierarchical, heterogeneous, modular architectures provide unified, interpretable solutions to multi-agent scalability, bridging theoretical multi-agent reinforcement learning research with real-world deployment requirements.

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 40th AAAI Conference on Artificial Intelligence, Singapore, 2026 January 20-27

Volume

40

First Page

41048

Last Page

41049

Identifier

10.1609/aaai.v40i48.42150

Publisher

AAAI Press

City or Country

Washington, DC, USA

Additional URL

https://ojs.aaai.org/index.php/AAAI/article/view/42150

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