AI RESEARCH
Characterizing MARL for Energy Control: A Multi-KPI Benchmark on the CityLearn Environment
arXiv CS.LG
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ArXi:2602.19223v2 Announce Type: replace-cross The optimization of urban energy systems is crucial for the advancement of sustainable and resilient smart cities, which are becoming increasingly complex with multiple decision-making units. To address scalability and coordination concerns, Multi-Agent Reinforcement Learning (MARL) is a promising solution. This paper addresses the imperative need for comprehensive and reliable benchmarking of MARL algorithms on energy management tasks.