df398b51d0
The IQN backward kernel now computes dL/d(h_s2) = dL/d(combined) ⊙ embed and outputs it to d_h_s2_buf [B, hidden_dim]. Previously this was explicitly NOT computed (comment: "trunk trained by C51"). Now the shared trunk receives BOTH gradient signals: C51: dense cross-entropy gradient (can be noisy/steep) IQN: bounded Huber quantile gradient (always stable) The IQN gradient stabilizes trunk training when C51's gradient is steep. With both signals, the trunk learns from C51's distributional knowledge AND IQN's risk-aware quantile knowledge simultaneously. New: d_h_s2_buf allocated in GpuIqnHead, zeroed before backward, accumulated via atomicAdd across all quantiles per sample. Accessors added: GpuDqnTrainer::bw_d_h_s2_buf(), shared_h2() 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;