7ab5d4fa71
When use_noisy_nets=true (the conservative() default), epsilon never decayed from 1.0 because (1) the trainer skipped update_epsilon() and (2) DQNAgentType::set_epsilon() was a no-op for RegimeConditional agents. This caused ALL training actions to be random — Q-values were learned but never used for action selection. Fix: set stored epsilon to 0.0 at training start when noisy nets are on. Exploration is provided by NoisyLinear weight perturbation + the separate noisy_epsilon_floor (5% safety floor for 45-action spaces). Also fixes: - Zstd-compressed .dbn file detection via magic bytes (0x28B52FFD) - Test data path resolution using ancestors().find() for workspace root - Test assertions updated for epsilon < 0.01 with noisy nets 2698 lib tests pass, 0 clippy warnings. 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;