c05f75d0d8
The hardcoded REWARD_SCALE_FLOOR=0.01 was tuned for smoketest (reward_std=0.007) but 940× too small for production (reward_std=6.57). The v_range floor must match the actual reward scale to ensure the Bellman projection can shift atoms meaningfully. - adapt_v_range_full takes observed reward_std from experience collector - EMA-smoothed reward_std (β=0.99) prevents single-epoch noise from jerking floor - Falls back to 0.01 before first observation, then adapts automatically - reward_std_ema field on GpuDqnTrainer, observed_reward_std on DQNTrainer - Decaying floor uses actual reward scale: floor = R_std * exp(-|Q_mean|/R_std) 903/903 tests passing. Smoketest val_Sharpe positive (60.76 epoch 1). 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;