TokamaCon: An Experiment-Derived Parallel Environment Suite for Tokamak Plasma Control

Real-time tokamak plasma control is a prerequisite for commercial magnetic-confinement fusion. Operating high-performance tokamak plasmas constitutes an extreme control challenge: controllers rely on sparsely indirect diagnostics, face distribution shift both within and across plasma discharges, adapt to multi-time-scale transport coupling and the effects of magnetohydrodynamic (MHD) instabilities, and obey hard safety limits where violations can terminate the discharge or damage hardware. Yet standard reinforcement-learning environments rarely expose this fusion-specific combination as a simultaneous, interacting control problem, while fusion-control studies lack reusable scenarios and metrics for studying it reproducibly. We introduce TokamaCon, which decouples tokamak real-time control into two representative environment slices, both grounded in EAST data and checked against reference simulators, thereby enabling the development and evaluation of reinforcement-learning algorithms for fusion-control problems. IsoFlux regulates magnetic equilibrium at 1 kHz on a high-frequency switching plant with input delay; Profile regulates kinetic profiles at 100 Hz on a stiff 1D transport PDE with instability-driven events. Both environments provide native PyTorch GPU-vectorised rollouts, reaching roughly $10^3$ IsoFlux and $10^3$ Profile environment steps per second at the recommended batch sizes on a single GPU. We release standardised scenarios, per-episode metrics, provenance metadata, and reference controllers for reproducible comparison of control approaches. Code and scenarios are released at https://anonymous.4open.science/r/TokamaCon_anonymous-CED5.