OpenG2G is a new extensible simulation platform that lets users implement and compare classic, optimization, and learning-based controllers for AI datacenter power flexibility coordinated with the grid.
Rl2grid: Benchmarking reinforce- ment learning in power grid operations.arXiv preprint arXiv:2503.23101
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
MARS-DA uses a top-level meta-controller to blend safe day-ahead allocation and real-time arbitrage sub-policies, delivering better risk-adjusted returns than baselines in a PJM-grounded two-settlement market simulator.
RNN-ProVe uses policy-driven sampling and statistical error bounds to produce high-confidence probabilistic estimates of behavioral violations in RNN policies for single- and multi-agent POMDPs.
PPO policy for grid topology control is distilled into decision trees and random forests that outperform the teacher on reward and survival time with lower inference cost and high interpretability.
citing papers explorer
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OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination
OpenG2G is a new extensible simulation platform that lets users implement and compare classic, optimization, and learning-based controllers for AI datacenter power flexibility coordinated with the grid.
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MARS-DA: A Hierarchical Reinforcement Learning Framework for Risk-Aware Multi-Agent Bidding in Power Grids
MARS-DA uses a top-level meta-controller to blend safe day-ahead allocation and real-time arbitrage sub-policies, delivering better risk-adjusted returns than baselines in a PJM-grounded two-settlement market simulator.
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Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning
RNN-ProVe uses policy-driven sampling and statistical error bounds to produce high-confidence probabilistic estimates of behavioral violations in RNN policies for single- and multi-agent POMDPs.
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Interpretable Policy Distillation for Power Grid Topology Control
PPO policy for grid topology control is distilled into decision trees and random forests that outperform the teacher on reward and survival time with lower inference cost and high interpretability.