dd6143936e
IQN kernel rewrite (critical — was corrupting 75% of gradient budget): - Eliminate compile-time BRANCH_*_SIZE defines (BRANCH_0_SIZE was 5, should be 3) - All 9 IQN kernels now take runtime b0/b1/b2/b3 params - 4-branch forward, backward, decode (was 3-branch, missing magnitude) - branch_actions buffer B*3 → B*4 Action diversity infrastructure: - 3-cycle counterfactual: dir mirror / mag relabel / order-type cost delta (50× amplified) - Per-branch entropy: order (d==2) gets 3× boost in C51 + MSE grad kernels - Position histogram expanded 6→12 bins (all 4 branches get entropy bonus) - Boltzmann temperature floor raised to 0.5 for order + urgency branches - Smoke test epochs 3→10 for diversity verification Note: experience collector still needs bottleneck forward path — currently reads trainer's bottleneck-layout weights with state_dim K, producing misaligned Q-values. Next: eliminate weight copy, use direct pointer views into trainer's params_buf. Co-Authored-By: Claude Opus 4.6 (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;