a2c14cb063
Layer A additive: three mapped-pinned buffers added to GpuDqnTrainer. - wiener_state_buf (141 floats) — Pearl D state (47 producers × 3 floats: sample_var, diff_var, x_lag). Per `feedback_no_htod_htoh_only_mapped_pinned`. - clamp_engage_per_block_buf (2048 ints) — Pearl C engagement counters (8 param-groups × 256 max blocks per Adam launch). - producer_step_scratch_buf (47 floats) — per-producer per-step step_observation output. Host applies Pearls A+D to map step_obs to ISV bound slot via pearls_ad_update (Task A3, pending). All three zero-initialized at construction (Pearl A sentinels). Reset registry entries follow in Task A12. No consumers wired yet — buffers are reserved but unread. Behavior unchanged. cargo check clean. 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;