2f193dca6d
The GPU experience collector gate checked only `dqn.dueling_q_network` (plain dueling), but with both `use_dueling: true` AND `use_distributional: true` (the defaults), DQN creates hybrid `dist_dueling_q_network` instead, leaving the plain dueling fields as None. This meant the GPU collector never initialized despite curiosity being enabled. Fix: Add else-if fallback to check `dist_dueling_q_network`/`dist_dueling_target_network` when plain dueling fields are None. Same fix applied to the weight sync site. Also fix test_train_with_empty_data_completes_gracefully: reduce to 5 epochs with early stopping disabled. The debug-mode async state machine is large enough that empty-data epochs run ~180ms each (vs ~3ms in release), triggering both plateau and patience-based early stopping. The test purpose is crash-freedom, not timing. 2497 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;