2bbc80cf46
PpoExperienceBatch: all 6 fields Vec<T>→CudaSlice<T>. Data stays on GPU from kernel collection through training. Zero DtoH for batch data. - collect_experiences(): returns CudaSlice via D2D copy, no memcpy_dtoh - gpu_batch_to_trajectory_batch(): DELETED (was CPU conversion) - PPO::update_gpu(): reads states via D2D mini-batch, forward on GPU - compute_metrics_from_gpu(): downloads only scalars (returns/advantages/ actions), NOT the 123 MB state tensor - download_*() methods for debug/checkpoint only States (123 MB/epoch) never leave GPU. Only 2 scalar losses come to CPU. 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;