387335e2b9
Quality-review follow-ups to commit 53bc0bc50 (nan_flags_buf 24->48):
1. Update run_nan_checks_post_forward docstring (gpu_dqn_trainer.rs:14842-14873):
replace 24-slot map with 48-slot range summary + audit-doc pointer.
Was actively misleading after the 24->48 expansion; partial-refactor
residue per feedback_no_partial_refactor.
2. Update '[24] system' comment in training_loop.rs (around line 2044):
reflect the 48-slot post-expansion state (slots 0-23 fwd, 24-35 bwd,
36-47 reserved). Also fix stale '0..11' tracing message to '0..47'.
3. Slot 31 (ensemble_d_logits_buf) annotation: flag DEFERRED + owner
on FusedDqnTraining (different struct than 24-30, 32-35). Prevents
Task 4 from blanket-launching check_nan_f32 on slot 31's null
accessor.
4. Both name-table header comments now reference the future
run_nan_checks_post_backward method (Task 4) plus the audit's
per-slot table — pre-empts contract drift when Task 4 lands a
3-way name-table dependency.
Audit-doc entry appended to docs/dqn-wire-up-audit.md SP1 Phase B
section. No behavioral change. Both name tables remain byte-identical.
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;