b6b17d46bb
Phase 3.2 lands the remaining two SP17 dueling-Q diagnostic producers
atomically with kernel + launcher + Rust wrapper + extended HEALTH_DIAG
emit + GPU oracle tests per `feedback_wire_everything_up`.
V_share[d] = |E[V]| / (|E[V]| + |E[A_centered, picked]|)
where picked = argmax_a Σ_z A_raw[i, a, z] (max-Q semantic — tractable
per-batch without depending on actions_history_buf which is collector-
time state stale relative to the cuBLAS forward at HEALTH_DIAG cadence).
Pearl-A bootstrap (sentinel 0.5) + α=WELFORD_ALPHA_MIN=0.4 + bilateral
[0, 1] clamp per `pearl_symmetric_clamp_audit`. 4 blocks × 256 threads.
advantage_clip_bound = p99(|A_centered|) × ADVANTAGE_CLIP_SAFETY_FACTOR=1.5
via sp4_histogram_p99 (block tree-reduce + per-warp tile binning, NO
atomicAdd per `pearl_fused_per_group_statistics_oracle`). EMA α=0.01
slow per-fold + bilateral clamp [0.1, 100.0] per
`pearl_symmetric_clamp_audit`. Pearl-A bootstrap (sentinel 1.0).
Single block × 256 threads + flat |A_centered| scratch buffer
(mapped-pinned, sized to B × Σ_d b_d × NA).
Observability-only — the actual clipping wire-up is Phase 5 follow-up.
The Phase 1 mean-zero contract (commits eabcf8d52..6f53d676f) makes
A_centered a meaningful signal; this commit observes it.
Extended HEALTH_DIAG line:
HEALTH_DIAG[N]: dueling [v_share=(d=X m=Y o=Z u=W)]
[a_var=(d=A m=B o=C u=D)] [clip=K]
GPU oracle tests on RTX 3050 Ti (all pass, 13/13 SP17 tests):
- v_share_per_branch_matches_closed_form: synthetic V=2.0 + linear A
per branch; closed-form V_share = 2/(2 + |K_d × (n_d-1)/2|);
ε=1e-4. Pearl-A bootstrap REPLACES on first launch.
- advantage_clip_bound_tracks_p99_safety: synthetic A with action-
dominant + per-(i,z) jitter (the jitter is REQUIRED — pathologically
lockstep values undercount in sp4_histogram_p99's non-atomic warp
tile binning per the kernel's documented "1/(256×32) loss for
uniformly distributed signals" qualifier; concentrated values violate
the assumption. Real |A_centered| in production is continuous, so
this is a test-data-only effect.) ε=0.20 (jitter + linear histogram
quantization).
Plan: docs/superpowers/plans/2026-05-08-sp17-dueling-q-network.md
Phase 3.2.
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;