Advantage-level Aggregation Reinforcement Learning for X-point Target Magnetic Configuration Control in an EXL-50U Experiment-Calibrated Simulation Environment

The study presents a reinforcement learning approach, Advantage Aggregation (AdvA), to control the X‑point target (XPT) divertor in the EXL‑50U tokamak experiment. By preserving objective‑wise temporal credit and applying a residual correction, AdvA‑PPO outperforms standard Reward‑PPO and a feedforward‑plus‑PID baseline, achieving a 0.81 worst‑channel score and reducing X‑point flux RMSE by roughly 20× over a 500 ms rollout. The method remains robust under measurement uncertainties and across varied initial equilibria, offering a simulation‑based foundation for future real‑time XPT validation on EXL‑50U.

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• reinforcement-learning
• fine-tuning