Dynamics-Informed Adaptive Offline RL for Real-Time Tokamak Plasma Control

Tracking plasma profiles, such as electron temperature and rotation, remains a challenging task in tokamak nuclear fusion. While RL-based approaches offer a promising path for multi-input-multi-output control systems, existing frameworks overlook two key obstacles: partial observability of plasma dynamics and the diagnostic mismatch between offline training and online execution. We propose a novel offline RL framework to address these challenges. Specifically, we train the control policy to condition on the latent context that is extracted from a trained plasma dynamics model. This dynamics-informed latent context encodes useful information about operating regimes, enabling the policy to adapt as the plasma evolves. To mitigate the gap between offline and real-time observation, we learn a converter that maps plasma diagnostics reconstructed offline to their real-time-observable counterparts, thereby exposing policy training to the real-time plasma diagnostics available in live experiments. In offline evaluations, the proposed method substantially improves tracking performance and robustness across a diverse set of discharges. We report real-time deployment results on DIII-D Tokamak Fusion Facility, showing feasibility under challenging actuator conditions.