eabcf8d529
Inserts per-atom mean-A reduction inside the per-branch outer loop: Q[a, z] = V[z] + (A[a, z] - mean_a A[*, z]) The post-centering atom-level identifiability holds: Σ_a A_centered[a, z] = 0 for every (sample, branch, atom). Per Wang et al. 2016 Dueling Networks: mean-zero is smoother and more stable than max-zero. Atom-level subtraction (not expectation-level) preserves distributional shape per action. Sample-local register reduction; no atomicAdd, no shared memory, no cross-thread sync. NUM_ATOMS_MAX=128 mirrors existing THOMPSON_MAX_ATOMS; device __trap() on overflow. ⚠ INTERIM STATE: c51_loss_kernel + c51_grad_kernel + mag_concat_qdir still read RAW (un-centered) advantage logits. Phase 1 Tasks 1.3-1.5 migrate them in lockstep within this branch BEFORE any L40S dispatch. A partially-migrated state is forbidden per feedback_no_partial_refactor. Plan: docs/superpowers/plans/2026-05-08-sp17-dueling-q-network.md Co-Authored-By: Claude Opus 4.7 (1M context) <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;