Эксперимент: Coordinate Path-Integral Joint Gibbs Policy (#466)

{ "worked": true, "confidence": 8, "verdict": "Built a readable two-agent coordinate-path Gibbs MVP with bounded-box quadrature, joint sampling, independent Gibbs baseline, path-order diagnostics, and finite-difference verification. The core predictions were confirmed: disagreement RMS matched |c-b|/3 within 0.5%, conservative fields had exactly zero path difference, and the gradient error was 2.9e-12. The coherent sampler also produced positive coordination (mean u1*u2=0.0618 versus -0.00048 independently), but this is only a toy signal, not an RL performance win.", "metrics": { "baseline": "Independent bounded Gibbs: mean product -0.00048; mean absolute product 0.13112.", "idea": "Coordinate-path joint Gibbs: mean product 0.06176; mean absolute product 0.14438. Path-disagreement RMS observed/predicted: 0, 0.0670/0.0667, 0.1675/0.1667, 0.3350/0.3333, 0.5025/0.5000. Gradient max error 2.86e-12." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only a two-dimensional analytic toy field was tested; no neural critics, automatic differentiation, multi-agent environment, replay training, RL return, collision rate, KL metric, runtime scaling, or GPU implementation was evaluated. The unbounded quadratic Gibbs covariance prediction was intentionally compared against a bounded domain and diverged at high temperature because boundary effects dominate." }