5d5a2c1f3d
Root cause: C51 distributional softmax structurally favors zero/low-variance actions (Flat for direction, Small for magnitude). This created irrecoverable feedback loops through FOUR paths: gradient, Bellman target argmax, action selection, and the Q-gap conviction filter. Direction branch (new): - Zero C51 gradient for direction (d==0) — same treatment as magnitude - Boltzmann softmax replaces argmax for direction selection (tau=2×Q_range) - 2× MSE gradient amplification for direction branch head - Mean advantage (not C51 softmax) for Bellman target argmax at d==0 - Q-gap conviction filter REMOVED from training path (redundant with Boltzmann, harmful after high-Sharpe epochs where Bellman max bootstrap raises Q(Flat) towards Q(directional), shrinking the gap) Magnitude branch (Bellman target fix): - Mean advantage for Bellman target argmax at d==1 in both MSE + C51 kernels - Proper softmax retained for online_eq and target_eq (learning objective) Metric fixes: - Diversity denominator: 9 → 7 (Flat forces mag=Half, max reachable is 7) - Threshold: 0.5% of total → 1% of directional (Flat-dominant policies mechanically killed diversity under the old metric) Result: 7/7 dir×mag diversity sustained epochs 7-10, Flat stable at 7-9% (was 60-87% growing). 19/19 smoke tests pass. 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;