0e61de408f
Single-block 256-thread CUDA kernel computing RMS(h_s2_aux [B, SH2]) and EMA-blending the step observation into ISV[H_S2_AUX_RMS_EMA_INDEX=449] directly. Pearl-A first-observation bootstrap embedded in kernel body (sentinel 0.0 → replace); fixed α=0.05 EMA blend thereafter. ISV slot 449 is outside the SP4/SP5 wiener buffer linear span so the scratch+apply_pearls_ad_kernel path is not available — self-contained Pearl-A logic mirrors the avg_win_hold_time_update_kernel precedent (slot 451). No atomicAdd; shmem block-tree-reduce only. Launched after aux_trunk_forward in the collector per-step hot path. - h_s2_aux_rms_ema_kernel.cu — new CUDA kernel (81 lines) - build.rs — cubin manifest entry - gpu_dqn_trainer.rs — H_S2_AUX_RMS_EMA_CUBIN static - gpu_aux_trunk.rs — HS2AuxRmsEmaOps struct + launch() - gpu_experience_collector.rs — field + constructor + hot-path launch - aux_trunk_oracle_tests.rs — h_s2_aux_rms_ema_pearl_a_bootstrap test - dqn-wire-up-audit.md — Phase C.6 audit entry cargo check -p ml --tests: clean (only pre-existing warnings) Oracle test: 1 new test added (requires GPU to run) 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;