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cs.MA2026
Cost of Structural Learning Under Censored Feedback: A Threshold-Bandit Approach
Michael Ledford, William Regli
In many multi-agent applications, tasks yield rewards only when executed by a coalition meeting an unknown size threshold; otherwise, feedback is fully censored. This censorship cr…
cs.MA2025
Learning to Coordinate Under Threshold Rewards: A Cooperative Multi-Agent Bandit Framework
Michael Ledford, William Regli
Cooperative multi-agent systems often face tasks that require coordinated actions under uncertainty. While multi-armed bandit (MAB) problems provide a powerful framework for decent…