3ad5e011b9
Comprehensive review of Layer B (commit 99367b9c6) caught one critical
and two important findings. All three fix in one atomic commit.
Critical: IQL Adam groups 4+5 (IqlHigh, IqlLow) missed the Pearl 4
migration. gpu_iql_trainer.rs::train_value_step still read
self.config.beta1=0.9, self.config.beta2=0.999 at runtime. ISV[230]
(adam_beta1(4)) and ISV[231] (adam_beta1(5)) were populated by the
Layer A producer but unconsumed — partial refactor of the 8-group
Pearl 4 contract. Fix mirrors the Curiosity pattern: 3 new
iql_beta1/beta2/epsilon parameters threaded through the IQL Adam call,
2 ISV reads at each of the 2 train_value_step call sites in
fused_training.rs.
Important: consumer clamp envelopes were wider than the SP4 producer
range (0.5..0.9999 vs producer's 0.85..0.95 for β1, etc.). At
cold-start with ISV=0, this produced β1=0.5 (more aggressive momentum
than the SP4-anchored 0.85 floor). Tightened all Pearl 4 consumer
clamps to match the producer envelope: β1∈[0.85,0.95], β2∈[0.99,0.9995],
ε∈[1e-10,1e-6].
Important: gpu_curiosity_trainer.rs:66-68 left dead ADAM_BETA1/BETA2/
EPS constants after the Pearl 4 migration. Removed.
8/8 Pearl 4 groups now ISV-driven (DqnTrunk, Value, Branches, IQN,
IqlHigh, IqlLow, Attn, Curiosity). cargo check + cargo build + lib
tests all clean.
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;