e89fce5eb2
GPU kernel produced raw percentage PnL rewards (~3.5e-4) which were ~10,000x smaller than Q-values (~7.0), making rewards invisible in the Bellman backup. Q-values froze at initialization. Port the CPU RewardNormalizer algorithm (EMA mean/variance, z-score normalization, [-3,3] clamp) directly into the CUDA kernel hot path as per-thread register state. Each thread maintains its own running mean/variance with configurable decay rate (reward_norm_alpha, default 0.01 = ~100-step window). EMA resets on episode boundaries. 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;