a225926e5f
9 slots: A_var_ema × 4 branches (per-branch advantage variance EMA) + V_share × 4 branches (V/(V+A) magnitude share) + adaptive advantage_clip_bound. All Pearl-A first-observation bootstrap with fold-reset registry entries + dispatch arms in trainer/training_loop.rs::reset_named_state. Bump ISV_TOTAL_DIM 474 → 483 + layout fingerprint seed. Per `feedback_isv_for_adaptive_bounds`: advantage_clip_bound is producer-tracked from p99(|A_centered|) × 1.5 safety factor, never hardcoded. Bounds [0.1, 100.0] are Category-1 dimensional safety floors. No consumer change in this commit (additive infrastructure). Phase 1 mean-centering producer (atomic across compute_expected_q + c51_loss + c51_grad + mag_concat_qdir) lands in the next 4 commits. 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;