65c3083dec
`target_ratio[CQL][b] = ANCHOR_CQL_RATIO * (1 - flatness[b])` had the sign inverted: it pushed CQL HIGH when Q was flat (the collapse state) and LOW when Q had variance. Per pearl_2_budget_kernel.cu, `flatness = var_q / σ²`, so flatness HIGH means Q has variance — exactly when overconfidence-risk-driven CQL pressure should kick in. Symptom (T10-v3 train-multi-seed-x7sl2 logs): Once IQN Fix 34 woke IQN-mag/ord/urg branches and produced real Q-targets, the controller's inverted formula sent cql_budget to MAX_BUDGET=1.0 saturation on every branch, the conservative pull kept Q flat, the model collapsed to single-Flat-action eval (val_active_frac=0, dir_entropy=0, trade_count=1, sharpe=0). Fix is one operational character: `(1 - flatness)` → `flatness`. C51 formula was already correctly aligned (`flatness * ANCHOR_C51_RATIO`) and unchanged. Per pearl_controller_anchors_isv_driven.md: the inverted formula was masked for months because the IQN-dead regime (Fix 34) suppressed cql_raw on mag/ord/urg, never letting the controller engage with the inverted target. Fixing the IQN regime exposed the dormant formula bug. ANCHOR_CQL_RATIO=2.0 and MAX_BUDGET=1.0 remain hardcoded; Fix 36 will make those ISV-driven via a GPU train_active_frac canary signal per the pearl's "pick the canary that fires under the pathology" rule.
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;