56374a43ac
DQN and PPO trainers now resolve hidden_dim_base from GPU VRAM when no explicit override is given, so L4/L40S/H100 GPUs use proportionally larger networks instead of being stuck at RTX 3050 Ti defaults (256). - Add resolve_hidden_dim_base() tiered lookup (256/512/768/1024 by VRAM) - Add network_param_count() for accurate model size estimation - DQN: pre-compute hidden dims, use real param count for batch sizing - PPO: add hidden_dim_base field, VRAM resolution in PpoTrainer::new() - PPO hyperopt: raise hidden_dim_base ceiling from 2048 to 4096 - TFT hyperopt: expand hidden_sizes from [128,256,512] to 5 tiers - Update stale estimates (DQN 50K→200K, PPO 100K→400K params) - Fix pre-existing clippy lints in prefetch.rs and ppo.rs 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;