b92dcc3dfc
Instrumentation from774d7552aserved its purpose — empirically identified the asymmetric reward cap at experience_kernels.cu:2788 as the inflater (popart min reaching -210336 by F0 ep3 pre-fix). Fixed in 35db31089; validated by smoke-test-trk72 (PASSED). Removed: - reward_chain_diag_reduce_kernel.cu - 12 per-sample diagnostic buffers in gpu_experience_collector - kernel parameter threading in experience_kernels.cu - launcher + reader + mapped-pinned output in gpu_dqn_trainer - wire-up site + HEALTH_DIAG emit in training_loop - audit doc section for the transient instrumentation This brings the worktree back to its pre-instrumentation state on the SP11 reward chain. Symmetric reward cap fix (35db31089) and plan_isv symmetric clamps (Commit A) remain. Co-Authored-By: Claude Opus 4.7 (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;