5106e3b117
Wires the trainer's `w_aux_to_q_dir [4]` device pointer into the GpuExperienceCollector so rollout-time action selection sees the active atom-shift for the direction branch. Changes: - gpu_experience_collector.rs: new field aux_w_to_q_dir_dev_ptr (u64, default 0) + setter set_aux_w_to_q_dir_ptr(). Both rollout launchers (compute_expected_q + quantile_q_select) now pass W ptr + exp_states_f32 + STATE_DIM_PADDED instead of NULL placeholders. NULL-safe via the kernels' existing aux_shift_active gating. - gpu_dqn_trainer.rs: w_aux_to_q_dir field promoted to pub(crate) for cross-module access via raw_ptr(). - trainers/dqn/trainer/training_loop.rs: new wire-up block after SP15 warm-count setter, mirroring the established setter pattern. NULL-safe on test scaffolds where fused_ctx or collector is absent. End-state — rollout activation: - compute_expected_q + quantile_q_select now return shifted E[Q] / quantile-blends per direction action during rollout. - With trainer's W trained each step (Step 8+11 Adam), the rollout policy's direction-action distribution actively reflects the learned aux→policy coupling. state_121's per-env value drives a per-(env, action) bias of magnitude W[a] (≤0.5 initial prior, learned thereafter). Verification: cargo check -p ml --lib clean (0 errors, 21 pre-existing warnings). Trainer + collector now smoke-ready end-to-end for the trainer/rollout side. Eval-side activation pending Phase D. 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;