d5e1214f25
Per feedback_cpu_is_read_only, saboteur effective scale now computed on-device: - saboteur_generate_params kernel signature gains isv ptr + saboteur_intensity_mult_slot parameter - Kernel reads `mult = fmaxf(isv[saboteur_intensity_mult_slot], SABOTEUR_MIN)` (sentinel-0 defense for cold-start before A2's controller first runs) and applies `effective_scale = base × mult` to perturbation generation - gpu_experience_collector.rs launcher updated; only one call site SimHash state_stride parameter added to lookup + update kernels — prepares for B1c replay-time curiosity wiring against trainer. states_buf which is STATE_DIM_PADDED=128-strided. Kernel inner loop reads `state[i × state_stride + d]` (was `i × 42 + d`). Proj-init kernel unchanged (writes projection, doesn't read states). Pre-requisite for B1c (curiosity wiring) and a small atomic step toward full SP11 production behavior. cargo check + build clean; 6/6 SP11 GPU tests + 14/14 contract tests still pass.
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;