Geometric Pareto Control embeds Pareto solutions in a Lie group submanifold and navigates via Riemannian gradient flow to achieve 100% feasibility and low suboptimality in control tasks without retraining.
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Offline RL policies trained solely on DIII-D historical data were deployed on the tokamak and produced promising real-world control of the plasma rotation profile.
citing papers explorer
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Geometric Pareto Control: Riemannian Gradient Flow of Energy Function via Lie Group Homotopy
Geometric Pareto Control embeds Pareto solutions in a Lie group submanifold and navigates via Riemannian gradient flow to achieve 100% feasibility and low suboptimality in control tasks without retraining.
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Offline Reinforcement Learning for Rotation Profile Control in Tokamaks
Offline RL policies trained solely on DIII-D historical data were deployed on the tokamak and produced promising real-world control of the plasma rotation profile.