80bc8a2ee1
Per-layer drop with 20% probability during training. Each hidden layer (h_s1, h_s2, h_v) is independently scaled by 0 (dropped) or 1/(1-p) (kept, expected-value correction). Only applied to online forward pass on current states — target and Double-DQN passes use full network. Implementation: - New stochastic_depth_scale kernel in dqn_utility_kernels.cu - Per-layer scale buffer [3] f32 — written by host before each graph replay (CUDA Graphs capture addresses, not contents) - Scale kernel inserted in launch_cublas_forward after online Pass 1 - update_stochastic_depth_mask() generates random 0/keep scales per step - At inference (experience collection), all layers active (no drop) Forces every layer to produce useful features independently — prevents deep compositional memorization where removal of any single layer would collapse the output. First RL trading application of stochastic depth (proven in Vision Transformers, novel in DQN). 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;