Research implementation of Meta-Learning (MAML) in Multi-Agent Reinforcement Learning (MARL) for power grid control using L2RPN. This project explores how meta-learning can improve agent adaptation across varying grid topologies and operational scenarios, aiming for safer and more efficient grid management.
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Research implementation of Meta-Learning (MAML) in Multi-Agent Reinforcement Learning (MARL) for power grid control using L2RPN. This project explores how meta-learning can improve agent adaptation across varying grid topologies and operational scenarios, aiming for safer and more efficient grid management.
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MorningStarTM/MetaMARL4GridTopo
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Research implementation of Meta-Learning (MAML) in Multi-Agent Reinforcement Learning (MARL) for power grid control using L2RPN. This project explores how meta-learning can improve agent adaptation across varying grid topologies and operational scenarios, aiming for safer and more efficient grid management.
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