Files
foxhunt/crates/ml
jgrusewski e7a6814045 feat(dqn): integrate GPU experience collector with weight sync
Adds GpuExperienceCollector field to DQNTrainer, initializes it when
dueling networks and curiosity module are present on CUDA device, and
syncs GPU weight copies after each training epoch.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-28 12:34:23 +01:00
..

ml

Machine learning models for Foxhunt.

Models

  • DQN (Rainbow) -- Deep Q-Network with prioritized experience replay, dueling heads, noisy nets, double Q-learning
  • PPO -- Proximal Policy Optimization with GAE, LSTM policies, clip-higher option
  • TFT -- Temporal Fusion Transformer for multi-horizon time series forecasting
  • Mamba2 -- State space model for efficient sequence prediction
  • Liquid Networks -- Biologically inspired neural networks for non-stationary data
  • TLOB -- Transformer-based Limit Order Book analysis
  • Flash Attention -- Optimized attention implementation

Training

Two paths per model:

  1. Standalone trainer -- direct training loop (e.g., DQN::train, PpoTrainer)
  2. UnifiedTrainable adapter -- wraps models for the hyperopt pipeline (e.g., DQNTrainableAdapter, UnifiedTrainablePPO)

Inference

InferenceAdapterBridge connects models to the ensemble coordinator in adaptive-strategy. Each model exposes an InferenceAdapter trait for prediction.

Backend

  • Candle v0.9.1 -- VarMap, AdamW, loss.backward(), GradStore, opt.step(&grads)
  • CUDA required for training -- tested on RTX 3050 Ti 4GB, max batch size 230
  • CPU inference supported

Hyperopt

ArgminOptimizer (Particle Swarm Optimization) with per-model adapters: DQN, PPO, ContinuousPPO, TFT, Mamba2. Uses ParameterSpace trait for continuous parameter mapping.

ModelType Enum

15 variants: CompactDQN, DistilledMicroNet, DQN, RainbowDQN, MAMBA, TFT, TGGN, LNN, TLOB, PPO, Transformer, Mamba, LiquidNet, TGNN, Ensemble.

Key Modules

dqn, ppo, tft, mamba, liquid, tlob, flash_attention, ensemble, evaluation, inference, trainers, hyperopt, checkpoint, preprocessing, data_loaders, features, model_factory, training_pipeline, regime_detection, stress_testing, validation, bridge, common, metrics.

Testing

SQLX_OFFLINE=true cargo test -p ml --lib  # ~2009 tests