5b9ef3fbe1
dqn-smoke: hardcoded (256,256,128,128) network dims → (64,64,32,32). With 256-wide layers, NoisyNet noise (sigma=2.0) can't overcome Q-value gaps even at high sigma. Smaller dims ensure noise dominates. dqn-early-stop: apply dqn-smoketest.toml profile for consistent hidden_dim across RTX 3050 and H100. Without it, H100 gpu profile sets hidden_dim=256 which changes gradient dynamics. dqn-collapse: v_range bound assertion 25.0 → 20.0. Phase Fast (default) fixes v_range to 20.0 from [phase_fast] TOML. The old assertion expected the unfixed (10.0, 50.0) range. 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;