4f6b4540c5
PopArt: normalize_rewards_popart_inplace was implemented but never called from run_full_step. Without it, rank-normalized rewards [-1,+1] spread across C51 atom range [-50,+50] — only 2% of atoms used, near-zero C51 gradient. Now called before forward pass (Phase 0a). Counterfactual: when do_flip=true AND cf_cycle==0 (directional mirror), cf_reward = -reward double-negated (reward already flipped at line 1895). Fix: cf_reward = do_flip ? reward : -reward. Affects ~1/6 of counterfactual experiences that were teaching wrong directional signal. 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;