05eb574d0c
Three fixes for GPU-accelerated Branching DQN training:
1. **GPU experience collector**: NoisyLinear creates standalone Vars via
Var::from_tensor(), bypassing VarMap registration. The GPU collector
looks up weights by name ("value_fc.weight") from VarMap and falls
back to CPU (~5x slower) when missing. Fix: register mu vars in
VarMap at construction, keep sigma vars standalone.
2. **Optimizer device mismatch**: Using only vars().all_vars() left
NoisyLinear head params frozen. backward() produces gradients the
optimizer doesn't know about → device mismatch in clip_grad_norm.
Fix: all_trainable_vars() = VarMap (shared+mu) + sigma.
3. **Single-threaded CPU bottleneck**: Runtime::new() creates a
current-thread scheduler → 1 OS thread → all async work serialized.
Fix: multi-thread runtime (4 workers) created once in DQNTrainer::new(),
shared across preload/training/backtest phases. Eliminates 3 fallback
Runtime::new() callsites.
Also: polyak_update_var_pairs with debug_assert_eq, two-phase target
network sync (VarMap Polyak + sigma var_pairs Polyak), copy_weights_from
handles NoisyLinear heads.
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;