9b6e94854f
Migrates `quantile_q_select` (uncertainty-driven action selection from
C51 atom CDFs) to the SP17 mean-zero advantage contract. The kernel
builds a per-(sample, branch, action) softmax over `V[z] + A[a, z]`
across THREE inner passes (max, sum_exp, CDF); all three now read
`adv_a[z] - a_mean_per_atom[z]`.
Plan flagged this as a hidden 6th consumer. Task 1.4/1.5 as authored
covered only c51_loss + c51_grad + mag_concat_qdir + Thompson; the
post-Task-1.2 audit found `quantile_q_select` reads raw advantage at
lines 5704/5709/5720 across all 4 branches and was missed entirely.
Sample-local register reduction (`float a_mean_per_atom[NUM_ATOMS_MAX]`,
NA_MAX=128 mirroring compute_expected_q); no atomicAdd, no shared mem,
no cross-thread sync per `feedback_no_atomicadd`. Device-side __trap()
on num_atoms overflow per `feedback_no_quickfixes`.
GPU oracle test: N=1, NA=3, B0=4 with V=[0,0,0] and A_raw chosen so the
per-atom mean is [0.75, 0, 0.75]. Test runs the production cubin with
iqn_readiness=0 and util_ema=1.0 and asserts each per-action q90 matches
the CPU oracle to ε=1e-5. Two structural-asymmetry assertions fail
loudly if centering regresses (action 0 q90 ≤ 0, action 3 q90 ≥ 0).
Mapped-pinned per `feedback_no_htod_htoh_only_mapped_pinned`.
Verification (RTX 3050 Ti):
cargo check --workspace → clean
cargo test -p ml --test sp17_dueling_oracle_tests --features cuda
-- --ignored → 3/3 PASS
⚠ INTERIM STATE: mag_concat_qdir + Thompson + aux-CQL barrier/ib still
read raw advantage. Commits B-D close them; Commit E annotates the two
already-centered c51_loss/c51_grad pre-SP17 sites. No L40S dispatch
escapes feat/sp17-dueling until every consumer is migrated per
`feedback_no_partial_refactor`.
Plan: docs/superpowers/plans/2026-05-08-sp17-dueling-q-network.md
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;