652d78f549
The expected_q kernel computed mean_adv as a SINGLE scalar averaged across ALL atoms AND actions, then subtracted from every logit. This destroyed per-atom advantage structure, making all actions produce nearly identical expected Q-values. Fixed to compute per-atom mean: mean_a(A[a,j]) separately for each atom j, matching the C51/MSE loss kernels' correct implementation. This bug affected: backtest evaluation action selection, Q-gap conviction filter (always 0 → floor 0.25), and Q-value monitoring. Training loss kernels were NOT affected (they had correct per-atom mean). 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;