864cf8690d
DQN: wire SAC-style entropy regularizer into TD loss, fix diversity penalty normalization from ln(3) to ln(45), add epsilon floor (0.05) for noisy nets, enable sigma scheduling (0.8→0.4 with 0.3 floor), add UCB count-based exploration bonus, expand hyperopt search space from 43D to 45D. PPO: replace fabricated entropy metric (value_loss×0.5) with real Shannon entropy from action distribution, fix EntropyRegularizer normalization from ln(3) to ln(45), implement Monte Carlo entropy estimate for flow policy (was returning zeros), fix hyperopt VRAM bound from 3 to 45 actions. Monitoring: add normalized action entropy and diversity Prometheus gauges, add exploration diagnostics logging per epoch. 2503 ml tests pass, 277 common tests pass, 0 clippy warnings. Co-Authored-By: Claude Opus 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;