13dd2e77bf
IQN graph now captures the FULL pipeline in one graph: - decode_actions + fwd/loss + backward + grad_norm + Adam - trunk gradient (cuBLAS backward into shared weights) - target EMA (tau from GPU-resident tau_buf, async HtoD before replay) - IQN→PER loss DtoD copy IQN EMA kernel changed: float tau → const float* tau_buf (device read). tau_buf added to GpuIqnHead with async cuMemcpyHtoDAsync per step. This was the last scalar parameter preventing full graph capture. regime_scale_td_errors moved into graph_adam submit sequence. Runs after Adam unflatten, before PER priority update. Per-step: 7 graph replays + ~9 ungraphed ops Ungraphed ops (genuinely can't be graphed — batch ptrs change): - upload_batch_gpu: 6 DtoD + 2 pad_states (batch-specific pointers) - HER relabel: 1-2 kernels (donor from batch next_states) - PER priority update: 1 kernel (indices from batch) 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;