Эксперимент: Intrinsic-Rank Filter Memory for Actor-Critic (#859)

{ "worked": true, "confidence": 8, "verdict": "Built a causal order-2 input/output filter lift with an SVD bottleneck and quadratic critic-regression MVP. Noiseless SIMO/MIMO ranks matched the predictions 4 and 6 exactly; noisy tail singular values scaled linearly with observation noise, and the cutoff transitioned from rank 6 to 8 across the noise floor. The bottleneck reduced the feature condition number dramatically, but critic test MSE was essentially unchanged, so the observed benefit is numerical stabilization rather than a clear task-performance win.", "metrics": { "baseline": "Full 8D noisy lift: test MSE 6.51326e-3; quadratic-feature condition 4.484e7.", "idea": "Rank-6 SVD bottleneck: test MSE 6.52388e-3; condition 5.860e1. SIMO observed rank 4/ambient 6, MIMO observed rank 6/ambient 8; noisy tail-to-signal ratio was approximately 0.1964 times noise amplitude." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.txt", "latest_run.txt" ], "limitations": "This is a synthetic linear-system verification, not a full actor-critic, SAC, or closed-loop policy-learning experiment. The filter bank uses fixed order-2 dynamics, only one main seed is reported for critic regression, and Procrustes alignment, online SVD updates, compute speed, and plant/model mismatch were not tested." }