195c051e7a
W structural prior init kernel (Step 5): - aux_w_prior_init_kernel.cu: 4-thread one-time init writing W[a] = [-0.5, 0.0, +0.5, 0.0] (Short / Hold / Long / Flat). Hardcoded values in kernel — no HtoD per feedback_no_htod_htoh_only_mapped_pinned.md. - build.rs registration + gpu_dqn_trainer.rs cubin static + handle field + new() launch after alloc_zeros for w_aux_to_q_dir. compute_expected_q atom-shift (Step 6): - experience_kernels.cu signature grows 4 args (w_aux, batch_states, aux_dir_prob_index, state_dim). NULL-safe — collapses to 0 shift when either pointer is NULL, bit-identical to pre-Phase-3-α. - Per-action inner loop computes aux_atom_shift = w_aux[a] * state_121 once per (b, a) for d==0 only. Inner z-loop applies z_val += aux_atom_shift before all S/TZ/TZ²/TLM accumulators consume it. Launcher updates (3 trainer sites + 1 collector site): - populate_q_out: passes W + current states (online path). - replay_forward_for_q_values: passes W + current states (online replay). - compute_denoise_target_q: passes W + next_states (target on s'; W shared across online/target). - gpu_experience_collector.rs: passes NULL W + NULL states until Phase C1 wires the trainer's W ptr through a setter. Architectural notes: - Action selection (every compute_expected_q call) now uses shifted atom positions for direction branch (d==0). Other branches stay bit-identical (shift = 0). Step 7 (c51_loss_kernel) + Step 8 (c51_grad_kernel) will close the loop on training loss + W gradient in the same atomic commit to avoid the gradient mismatch trap per feedback_no_partial_refactor.md. - Adam wireup for w_aux_to_q_dir lands at Step 11; until then W stays at structural prior values (no Adam step modifies it). Verification: - cargo check -p ml --lib: 0 errors, 21 pre-existing warnings (Phase 3b baseline parity). - Audit doc updated with B9 Step 5+6 checkpoint entry. 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;