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arXiv Machine Learning By Chuhao Qin, Evangelos Pournaras

Optimization without Future Compromises? Decentralized Coordination via Collective and Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper introduces Hierarchical Reinforcement and Collective Learning (HRCL), a framework that combines multi‑agent reinforcement learning (MARL) with decentralized coordination. HRCL uses MARL at a high level to generate strategic guidance that limits the decision space for low‑level agents, enabling efficient short‑term coordination while considering long‑term effects. Experiments on synthetic, energy‑management, and drone‑swarm scenarios demonstrate faster convergence and significant reductions in system‑wide and individual costs compared to standalone MARL.

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