efeb950751
- 1 constructor site (rng_states u32 init) rewritten to mapped_pinned::upload_u32_via_pinned. - 2 warm-path sites (source_indices, donor_indices in relabel_batch) rewritten to mapped_pinned::upload_i32_via_pinned. The warm sites re-allocate pinned staging per relabel call — acceptable on this cold path (HER relabel runs once per epoch). Adds MappedU32Buffer type to mapped_pinned.rs mirroring MappedI32Buffer, plus upload_u32_via_pinned helper. Manual Debug impl so warning count stays at 13. Audit row appended (Fix 10) in docs/dqn-gpu-hot-path-audit.md. 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;