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.
Deep differentiable reinforcement learning for generic energy storage day-ahead bidding with transformer neural networks,
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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.