GradMAP enables fast offline training of fully decentralized neural policies for grid-edge flexibility by embedding a differentiable three-phase AC power-flow model and applying proximal surrogates in action space.
Reinforcement learning for electric vehicle applications in power systems: A critical review
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Nash-MADDPG combines Nash bargaining with MADDPG to coordinate V2V energy trades, yielding 61.6% higher social welfare and 40.1% better Jain fairness than double auctions in 30-day simulations with 6-100 agents.
citing papers explorer
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GradMAP: Gradient-Based Multi-Agent Proximal Learning for Grid-Edge Flexibility
GradMAP enables fast offline training of fully decentralized neural policies for grid-edge flexibility by embedding a differentiable three-phase AC power-flow model and applying proximal surrogates in action space.
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Incentive-Aligned Vehicle-to-Vehicle Energy Trading via Nash-Integrated Multi-Agent Reinforcement Learning
Nash-MADDPG combines Nash bargaining with MADDPG to coordinate V2V energy trades, yielding 61.6% higher social welfare and 40.1% better Jain fairness than double auctions in 30-day simulations with 6-100 agents.