🖼
Extended Data Fig. 3 — Magnetic control of tokamak plasmas through deep reinforcement learning.
active
paper figure
Created: 2026-04-21T18:29:40
By: paper_figures_tool
Quality:
50%
🔗 External
ID: paper-fig-paper-ca17ff7c4a73-9
Extended Data Fig. 3Figure 9
Control variability. To illustrate the variability of the performance that our deterministic controller achieves on the environment, we have plotted the trajectories of one policy that was used twice on the plant: in shot 70599 (in blue) and shot 70600 (in orange). The dotted line shows where the cross sections of the vessel are illustrated. The trajectories are shown from the handover at 0.0872 s until 0.65 s after the breakdown, after which, on shot 70600, the neutral beam heating was turned on and the two shots diverge. The green line shows the RMSE distance between the LCFS in the two experiments, providing a direct measure of the shape similarity between the two shots. This illustrates the repeatability of experiments both in shape parameters such as elongation κ and triangularity δ and in the error achieved with respect to the targets in plasma current I p and the shape of the last closed-flux surface.
Source data
▸Metadata
| pmid | paper-ca17ff7c4a73 |
| caption | Control variability. To illustrate the variability of the performance that our deterministic controller achieves on the environment, we have plotted the trajectories of one policy that was used twice |
| image_url | https://www.ebi.ac.uk/europepmc/articles/PMC8850200/bin/41586_2021_4301_Fig7_ESM.jpg |
| paper_title | Magnetic control of tokamak plasmas through deep reinforcement learning. |
| figure_label | Extended Data Fig. 3 |
| figure_number | 9 |
| _schema_version | 1 |
| source_strategy | pmc_api |
📊 Evidence Profile
Evidence Balance
+0%
Certainty
0%
Debates
0
Incoming
0
Outgoing
0
0 supporting
0 contradicting
0 neutral
Public annotations (0)Annotate on Hypothes.is →
No public annotations yet.