Self-Supervised Amortized Mean-Field Controller / results.json
Mechanism confirmed, baseline not beaten
1{
2 "math_check": {
3 "gamma": [
4 0.0,
5 0.05,
6 0.1,
7 0.2,
8 0.4
9 ],
10 "variance_slope_observed": [
11 0.0,
12 0.4083374166666667,
13 0.8166748333333333,
14 1.6333496666666667,
15 3.2666993333333334
16 ],
17 "variance_slope_predicted_2gamma": [
18 0.0,
19 0.1,
20 0.2,
21 0.4,
22 0.8
23 ],
24 "euler_excess_variance_observed": [
25 2.4666993333333336,
26 2.4697839835544264,
27 2.472871185884375,
28 2.479053246870833,
29 2.4914479941499996
30 ],
31 "euler_excess_variance_predicted": [
32 0.0,
33 3.125e-07,
34 1.25e-06,
35 5e-06,
36 2e-05
37 ],
38 "identity_abs_errors": [
39 0.3083374166666667,
40 1.2333496666666668,
41 2.4666993333333336
42 ],
43 "zero_gamma_correction": 2.4666993333333336
44 },
45 "controller_experiment": {
46 "device": "cuda",
47 "amortized_pretraining_seconds": 84.64497542381287,
48 "amortized_mean_cost": 0.728020585840568,
49 "amortized_mean_mean_error": 0.05318319221260026,
50 "amortized_mean_variance_error": 0.08519219467416406,
51 "independent_90step_mean_cost": 0.3131400402635336,
52 "independent_mean_error": 0.01835873955860734,
53 "independent_variance_error": 0.004139695316553116,
54 "independent_seconds_per_task": 11.656281918287277,
55 "note": "Costs are direct Monte Carlo rollout objectives; no target trajectories are used."
56 }
57}