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Conference proceeding
Collaborative Information Dissemination with Graph-Based Multi-Agent Reinforcement Learning
Algorithmic Decision Theory 8th International Conference, ADT 2024, New Brunswick, NJ, USA, October 14–16, 2024, Proceedings, pp.160-173
Lecture Notes in Artificial Intelligence
ADT 2024: Algorithmic Decision Theory, 8th (New Brunswick, New Jersey, USA, 10/14/2024–10/16/2024)
01/2025
Web of Science ID: WOS:001414141200011
Abstract
Efficient information dissemination is crucial for supporting critical operations across domains like disaster response, autonomous vehicles, and sensor networks. This paper introduces a Multi-Agent Reinforcement Learning (MARL) approach as a significant step forward in achieving more decentralized, efficient, and collaborative information dissemination. We propose a Partially Observable Stochastic Game (POSG) formulation for information dissemination empowering each agent to decide on message forwarding independently, based on the observation of their one-hop neighborhood. This constitutes a significant paradigm shift from heuristics currently employed in real-world broadcast protocols. Our novel approach harnesses Graph Convolutional Reinforcement Learning and Graph Attention Networks (GATs) with dynamic attention to capture essential network features. We propose two approaches to accomplish cooperative information dissemination, L-DyAN and HL-DyAN, differing in terms of the information exchanged among agents. Our experimental results show that our trained policies outperform existing methods, including the state-of-the-art heuristic, in terms of network coverage and communication overhead on dynamic networks of varying density and behavior.
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Details
- Title
- Collaborative Information Dissemination with Graph-Based Multi-Agent Reinforcement Learning
- Publication Details
- Algorithmic Decision Theory 8th International Conference, ADT 2024, New Brunswick, NJ, USA, October 14–16, 2024, Proceedings, pp.160-173
- Resource Type
- Conference proceeding
- Conference
- ADT 2024: Algorithmic Decision Theory, 8th (New Brunswick, New Jersey, USA, 10/14/2024–10/16/2024)
- Publisher
- Springer Nature
- Series
- Lecture Notes in Artificial Intelligence
- Number of pages
- 14
- Copyright
- © 2025 The Author(s), under exclusive license to Springer Nature Switzerland
- Identifiers
- WOS:001414141200011; 99381474385206600
- Academic Unit
- Institute for Human and Machine Cognition; Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English