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Optimization without Future Compromises? Decentralized Coordination via Collective and Reinforcement Learning
Efficient resource allocation in multi-agent systems requires autonomous agents to coordinate their decisions while balancing system-wide objectives with individual costs. This becomes increasingly challenging over long time horizons, where decisions that improve the current allocation may compromis
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