6289710463
Cherry-picking Phase 3 (a8731dda4+84929f419) onto Phase 2 fix tip (13ce78385) with --strategy-option=theirs dropped is_win_per_env arg + field + registration the wr_ema fix added. Manual restoration: - experience_kernels.cu: re-added is_win_per_env arg to experience_env_step - gpu_experience_collector.rs: re-added struct field, alloc, ctor entry, launcher arg - state_reset_registry.rs: re-added FoldReset RegistryEntry - training_loop.rs: re-added named-reset dispatch arm - docs/dqn-wire-up-audit.md: documented the merge restoration Test sp20_is_win_per_env_registered_fold_reset passes. Workspace + examples compile clean. wr_ema runtime mystery (fix wired, but production still shows wr_ema=0) unchanged — debug per13ce78385scaffold pending. 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;