# Эксперимент: Topological Reachable-Set Coverage Scheduler (#836)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a readable 2D reachable-set coverage scheduler with random and adaptive/farthest sampling, exact KD-tree coverage measurement, and reproducible sweeps. The Lipschitz safety bound held in 80/80 checks, residual-gap scaling was linear in L with constant gap/L=0.141421, and adaptive sampling reduced median coverage radius and reached certification substantially sooner for feasible thresholds. This validates the mechanism in the controlled toy setting, but not yet in a learned neural-policy or physical rollout setting.",
  "metrics": {
    "baseline": "Random sampling: median coverage radius at 12/25/50/80 samples = 0.43013/0.32473/0.22326/0.17309; certification median samples = 93 at L=0.25 and 266.5 at L=0.5 among successful runs.",
    "idea": "Adaptive sampling: median coverage radius at 12/25/50/80 samples = 0.32998/0.18856/0.10541/0.10000; certification median samples = 36 at L=0.25 and 88 at L=0.5. Bound validity was 100% (80/80), and median residual gap/L stayed 0.141421 across L=0.25,0.5,1,2,4."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 coverage_scheduler_experiment.py",
  "files": [
    "coverage_scheduler_experiment.py",
    "results.json",
    "REPORT.md"
  ],
  "limitations": "The experiment uses a known static 2D unit-square reachable set and an analytically Lipschitz residual on a finite grid. It does not test a learned policy, simulator-generated trajectories, estimated reachable-set error, noisy residuals, high-dimensional metrics, latent reconstruction bounds, or confidence intervals; the low-margin score was inactive because all residuals exceeded epsilon."
}