e07c82976e
When health=1 (healthy): tau unchanged → sharp softmax → near-argmax target (deterministic). When health=0 (collapsed): tau scales 6× → wide softmax → stochastic sampling breaks Q-collapse attractor. - c51_loss_kernel.cu: add get_learning_health() helper, isv_signals as last param, tau_base → tau * factor - gpu_dqn_trainer.rs: add last_sarsa_tau_factor field + init, launch_c51_loss → &mut self, host-side mirror, append isv_signals_dev_ptr arg - fused_training.rs: add last_sarsa_tau_factor() accessor - training_loop.rs: propagate SARSA tau factor from fused ctx for HEALTH_DIAG logging Co-Authored-By: Claude Sonnet 4.6 <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;