Эксперимент: Tangential Bellman Tie Resolver (#785)

{ "worked": true, "confidence": 8, "verdict": "Built a finite-state Tangential Bellman Tie Resolver with value iteration, softmax branch selection, contraction checks, and reproducible scaling sweeps. The mechanism manifested: fixed-point error ratios matched beta across beta={0.2,0.5,0.8,0.95} to within about 5e-7, and resolver transition widths scaled exactly as 1/N, with widthN approximately 0.769 and normalized-curve deviation 6.4e-14. This is a strong toy-level confirmation, though not evidence of an RL performance win.", "metrics": { "baseline": "Noisy hard argmin transition width = 0.1331 for every N; widthN = {2.662, 6.655, 13.310, 26.620}. Under perturbation 0.03 and Gaussian noise sigma=0.06, best-branch selection frequency was 0.2077.", "idea": "Tangential resolver transition widths for N={20,50,100,200} = {0.03846, 0.01538, 0.00769, 0.00385}; width*N = {0.76914, 0.76914, 0.76914, 0.76914}. Normalized curve max deviation was 6.35e-14. Soft resolver probability of the best branch under the direct noisy test was 0.2256." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 tie_resolver_experiment.py", "files": [ "tie_resolver_experiment.py", "results.json" ], "limitations": "Only a synthetic finite-state MDP was tested; no continuous-action critic, learned dynamics ensemble, SAC integration, replay data, environment benchmark, compute/FLOP comparison, or statistically repeated RL training was performed. The N-scaling experiment uses a controlled temperature rule tau=0.35/N and therefore verifies predicted mathematical scaling rather than estimating an empirical extreme-value exponent from sampled actions." }