d346e03d48
epsilon_update kernel computes effective epsilon from ISV[EPOCH_IDX=39, TOTAL_EPOCHS=40, LEARNING_HEALTH=12] with cosine schedule (eps_start -> eps_end) plus health-coupled boost (up to +0.1 at collapse). Single-thread cold-path kernel writes ISV[EPSILON_EFF_INDEX=41]. EpsilonMonitor is a read-only observer exposing eps_eff, epoch_idx, total_ep, health, progress, fire_rate in DiagSnapshot. Wires epsilon kernel launch at epoch boundary alongside tau kernel. Consumer migration: log_training_config ISV-adaptive epsilon block now reads ISV[EPSILON_EFF_INDEX] instead of computing `base_floor * (0.5 + volatility)`. Tests: 3 monitor unit tests pass. cargo check -p ml at 8-warning baseline. Plan 1 Task 14. Spec §4.C.6 (2026-04-24 revision). 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;