0ab54dc8ef
T15: training rewrite mirroring T14 eval. N_par parallel envs lockstep H steps per epoch; ONE batched C51 update at B = N_par * H. Expected ~15× speedup vs sequential. - NEW kernel alpha_h_enriched_store_batched_kernel for batched h_enriched slot writes - Training section greenfielded: legacy sequential loop deleted - CLI flag --n-train-par (default 50) - Terminal next-state slot zeroed; done=1 at horizon masks Q_next contribution in Bellman projection — no terminal Mamba2 forward - docs/isv-slots.md updated per kernel-audit-doc hook Co-Authored-By: Claude Opus 4.7 <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;