# Эксперимент: Online Taylor Residual World Model (#1317)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built an online Taylor residual world-model module with explicit monomial features and recursive least squares updates. RLS matched batch least squares closely, and the forgetting-factor test showed faster recovery for lower lambda at the cost of higher variance. In the frozen-MLP toy dynamics experiment, residual adaptation substantially improved one-step and 10-step errors; degree 3 performed best in this smooth setting, but this is not evidence of an MPC or real-world control win.",
  "metrics": {
    "baseline": "Frozen MLP: one-step MAE 0.2046, early post-shift MAE 0.2618, 10-step rollout MAE 0.6165",
    "idea": "Degree 1: 0.0495 / 0.0655 / 0.1436; degree 2: 0.0331 / 0.0428 / 0.1103; degree 3: 0.0230 / 0.0282 / 0.0770 for one-step / early post-shift / 10-step MAE. Forgetting test: lambda=0.90 recovered at step 49 versus 150 for lambda=0.99, with post-adaptation standard deviation 0.0154 versus 0.0024."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 online_taylor_experiment.py",
  "files": [
    "online_taylor_experiment.py",
    "results.json"
  ],
  "limitations": "Only a small deterministic 1D synthetic system was tested, with a frozen scikit-learn MLP and random open-loop inputs. No CartPole/Pendulum benchmark, MPC controller, GPU implementation, recentering logic, multi-output physical model, measurement-noise sweep, or repeated-seed statistical comparison was tested."
}