SACHI enriches agent representations via graph transformer convolutions over inter-agent graphs to enable holistic information integration, outperforming baselines across five cooperative tasks with statistical significance.
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In experience-constrained federated RL for UAVs, learning performance depends primarily on experience reuse and minibatch size rather than the number of participating learners.
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SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning
SACHI enriches agent representations via graph transformer convolutions over inter-agent graphs to enable holistic information integration, outperforming baselines across five cooperative tasks with statistical significance.
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Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments
In experience-constrained federated RL for UAVs, learning performance depends primarily on experience reuse and minibatch size rather than the number of participating learners.