{"ID":3083726,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T03:54:17.966829144Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.06162","arxiv_id":"2606.06162","title":"Learning to Contest: Decentralized Robust Fairness in Cooperative MARL via Cross-Attention","abstract":"Fair cooperative multi-agent RL (MARL) teams maximizing egalitarian welfare are exploitable: a single selfish agent free-rides on the surplus fair agents forgo to raise the worst-off. A centralized need-based allocator removes it, but only by taking allocation out of agents' hands; whether decentralized policies can be robust was left open. We show this futility is an artifact of all-or-nothing contention. Under graded contention (a contested resource delivers $1-c$, wasting $c$), we prove that for any $c\u003c1$ a worst-off cooperator that contests a free-rider strictly improves on yielding, so decentralized leverage exists (Prop. 1). Realizing it is a coordination problem under uncertainty: the number of free-riders is unknown and variable, so any fixed rule is dominated. We introduce CAN, a permutation-equivariant cross-attention policy over agents' observed behaviour that infers the number of free-riders and responds proportionally: turn-taking when none, contesting just enough when some. Trained against an adversarial league (PSRO), CAN keeps best-response exploitability low ($ρ\\approx1.2$-$1.5$, vs. $ρ=N$ unprotected) across the contention range, wasting almost nothing at $D=0$ (efficiency $\\approx1.0$) and retaining most of it at $D\\geq1$ (efficiency 0.83-0.96), approaching the centralized oracle on both axes, no central allocator. Fair-MARL learners fail on complementary axes (GGF/FEN yield and are exploitable, SOTO all-contests and wastes), while CAN is both. On two further games we find clear scope, not blanket generality: CAN stays efficient and Pareto-dominates the fair learners, but its robustness holds only in proportion to the contest leverage: strong on a multi-server game, partial when it weakens, absent under winner-take-all (Prop. 1 fails). We also report its fragilities: weak leverage and zero-shot transfer to larger teams degrade it at high contention.","short_abstract":"Fair cooperative multi-agent RL (MARL) teams maximizing egalitarian welfare are exploitable: a single selfish agent free-rides on the surplus fair agents forgo to raise the worst-off. A centralized need-based allocator removes it, but only by taking allocation out of agents' hands; whether decentralized policies can be...","url_abs":"https://arxiv.org/abs/2606.06162","url_pdf":"https://arxiv.org/pdf/2606.06162v1","authors":"[\"Can Savcı\"]","published":"2026-06-04T13:34:50Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.GT\"]","methods":"[]","has_code":false}