2e06d86e43
The C51+MSE gradient blending passed the same pointer as both y (output) and x (input) to dqn_saxpy_f32_kernel. That kernel has __restrict__ on both parameters, promising the compiler they don't alias. When they DO alias, NVCC's no-alias optimizations produce undefined behavior (NaN from register corruption). Added dqn_scale_f32_kernel (y[i] *= alpha) — single-pointer variant with no __restrict__, no aliasing possible. The two self-aliased SAXPY calls are replaced with scale+SAXPY pairs using distinct pointers. 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;