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Trust-Based Information Filtering for Robust Decentralized Execution of Pre-Trained MARL Policies in UAV Swarms
Rudzitis, Ernests (2025) Trust-Based Information Filtering for Robust Decentralized Execution of Pre-Trained MARL Policies in UAV Swarms.
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| Abstract: | Multi-Agent Reinforcement Learning (MARL) enables complex drone swarm behaviors; however, the mission success is hindered by unreliable communication. Existing robustness solutions often require integration during training or significant configuration, limiting flexibility. This paper introduces Trust-Based Information Filtering (TIF) system that enhances pre-trained MARL policies during decentralized execution. The post-hoc TIF system equips each agent with a mechanism to assess message trustworthiness using learned spatio-temporal expectations from normal operations. This dynamic self-configuration eliminates the need for attack data or policy retraining. Evaluated in UAV formation control under various communication unreliability scenarios, TIF demonstrates a measurable improvement in operational resilience. This validates the prototype of effective, lightweight, post-hoc filtering approach, signaling that robustness can be layered onto existing MARL policies without costly retraining. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/107311 |
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