ab3e17f4a2
Per-branch sigma scales with per-branch Q magnitude (v_half from Pearl 1's ATOM_V_HALF, populated in A1). SIGMA_FRACTION adapts via entropy-deficit controller targeting 70% of max action entropy (target_entropy = log(n_actions[b]) * 0.7). 8 ISV slots (NOISY_SIGMA[210..214), SIGMA_FRACTION[214..218)). Reuses BRANCH_ENTROPY (218..222) from Task A1's q_branch_stats_kernel. producer_step_scratch_buf grew 103 -> 111 (8 new outputs). wiener_state_buf already at 543 (A1 sized for entire SP5 block). NoisyLinear consumer migration deferred to Layer B. Co-Authored-By: Claude Sonnet 4.6 <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;