76d58406bd
Brings in worktree-agent-a4d8a879 (commit ed3fa066b): per-branch σ via
[4]-element mapped-pinned device buffer. add_advantage_noise kernel
indexes σ by branch derived from action_idx % total_actions; Q-value
layout is branch-major contiguous so per-branch σ derivation requires
no forward-pass restructuring.
3 ExperienceCollectorConfig constructors updated.
Resolves Pearl 3 averaging from SP5 Layer B which collapsed 4 per-branch
σ values into a single scalar via training_loop.rs:1747.
Co-Authored-By: Claude Sonnet 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;