c1b6848c21
gamma_update kernel computes health-coupled gamma from ISV[LEARNING_HEALTH=12]: gamma_eff = gamma_min + (gamma_base - gamma_min) * health. Single-thread cold-path kernel writes ISV[GAMMA_EFF_INDEX=43]. GammaMonitor is a read-only observer exposing gamma_eff, health, fire_rate. Consumer migration: fill_gamma_buf and IQL gamma computation now read ISV[GAMMA_EFF_INDEX] via read_isv_signal_at (pinned, zero-copy). Training loop passes hyperparams.gamma as gamma_base to the kernel. Deleted: apply_adaptive_gamma method (GpuDqnTrainer + FusedTrainingCtx delegates), set_adaptive_gamma method, adaptive_gamma field (GpuDqnTrainer + DQNTrainer), last_gamma_eff cached field + last_gamma_eff() delegate. StateResetRegistry entry for adaptive_gamma removed (field gone). Smoke test generalization.rs updated to check config gamma_base instead of deleted adaptive_gamma field. Tests: 3 monitor unit tests pass. cargo check -p ml at 8-warning baseline. Plan 1 Task 10. 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;