148d4cbb43
Enable curiosity module by default (weight 0.0→0.1) to satisfy the three-way gate (dueling + target + curiosity) that was blocking the GPU experience collector CUDA kernel. This eliminates the 30-40% CPU experience collection phase that was the main GPU idle bottleneck. Additional changes: - BatchSample API: train_step/compute_gradients now accept Option<BatchSample> instead of Option<Vec<Experience>>, preserving PER importance-sampling weights and indices through pre-sampling. - Async PER pre-sampling: train_step_single_batch and train_step_with_accumulation now pre-sample from the replay buffer using a READ lock before acquiring the WRITE lock for GPU training. This separates CPU sampling (~250μs) from GPU forward/backward (~3ms). - Delete dead EpochPrefetcher: the binary (train_baseline_rl.rs) already implements fold prefetching with background thread + mpsc channel + GPU double-buffering, making the trainer's EpochPrefetcher redundant. - Hyperopt DQN bounds: curiosity_weight min 0.0→0.01 so PSO can never fully disable curiosity (which would re-gate the GPU collector). 10 files changed, -195 net lines. 2497 tests pass, 0 clippy warnings. Co-Authored-By: Claude Opus 4.6 <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;