A centrally coordinated multi-agent architecture that decouples regional action proposals from a coordinating selector improves sample efficiency over single-agent RL in L2RPN power grid benchmarks.
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Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control
A centrally coordinated multi-agent architecture that decouples regional action proposals from a coordinating selector improves sample efficiency over single-agent RL in L2RPN power grid benchmarks.