a0aab3baa5
H100 baseline (20 epochs) showed gradient collapse at epoch 10: C51 cross-entropy converges its distributional fit before the policy converges, leaving zero gradient signal. The collapse happened 5 epochs after C51 reached alpha=1.0 (pure C51, zero MSE). Fix: cap the C51 alpha ramp at c51_alpha_max (default 0.5) so MSE always contributes (1 - alpha_max) of the primary gradient. MSE loss measures Q-error directly and only goes to zero when Q-values are correct, not just when the distribution shape is right. - c51_alpha_max added to DQNHyperparameters (default 0.5) - Added to PSO search space as 15th dimension (range [0.3, 0.9]) - Added to TOML profiles: smoketest, production, hyperopt - Training loop caps alpha ramp at alpha_max instead of 1.0 - All 907 ml tests + 300 ml-core tests + 6 smoke tests pass Co-Authored-By: Claude Opus 4.6 (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;