1e70cd5e59
Phase 3 of SP17 dueling-Q identifiability — first of three diagnostic
producers landed atomically with kernel + launcher + Rust wrapper +
HEALTH_DIAG emit + GPU oracle test per `feedback_wire_everything_up`.
Per branch d ∈ {dir, mag, ord, urg}:
Var_d = (1/(B × n_d × NA)) Σ_{i, a, z} (A[i, a, z] − mean_a A[*, z])²
Block tree-reduce (no atomicAdd, `feedback_no_atomicadd`); 4 blocks ×
256 threads. Pearl-A first-observation bootstrap (sentinel 0.0 →
REPLACE on first launch); steady-state α = WELFORD_ALPHA_MIN=0.4 per
`pearl_wiener_alpha_floor_for_nonstationary` — the structural-control
floor preserves catch-up bandwidth without storing 24 Welford
accumulator slots for a cold-path-cadence diagnostic.
Cold-path emit: single launch per HEALTH_DIAG cadence (epoch boundary)
right after `v_a_means`. New line:
HEALTH_DIAG[N]: dueling [a_var=(d=X m=Y o=Z u=W)]
The line will be extended with V_share + advantage_clip_bound in
Phase 3.2, then finalised in Phase 3.3.
GPU oracle test on RTX 3050 Ti: synthetic A constructed so each branch
d has a closed-form Var(A_centered); kernel readback matches expected
value within ε=1e-4 (f32 rounding budget for ~8×n×51 accumulator
length).
Plan: docs/superpowers/plans/2026-05-08-sp17-dueling-q-network.md
Phase 3.1.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;