a51345a141
Layer 1: Per-sample CE clamped to MAX_PER_SAMPLE_CE=50 before IS weight. Breaks PER feedback loop: high loss → high priority → high IS weight → repeat. Layer 2: Bellman target smoothed with ε=0.01 uniform mix after projection. Prevents any atom from having zero probability → no log(near-zero) in CE. Root cause: C51 cross-entropy is unbounded when target and predicted distributions are maximally misaligned. PER amplifies pathological samples. These two layers cap the maximum possible CE and prevent the extreme misalignment from occurring. 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;