4b40710b7c
Phase B5b's K=3 input concat passed plan_params=NULL because the collector had no trade plan launch. Trainer-side K=3 forward trained on real plan_params while the collector queried at plan_params=0 — documented train/inference asymmetry on the plan-conditioning surface. Phase B5b-2 mirrors the (now-corrected) trainer `launch_trade_plan_forward` chain on the collector inside the rollout step: 1. SGEMM: hidden[N, AH] = h_s2_q[N, SH2] @ W_fc[AH, SH2]^T 2. bias+relu in-place on hidden 3. SGEMM: pre_out[N, 6] = hidden[N, AH] @ W_out[6, AH]^T 4. trade_plan_activate → exp_plan_params[N, 6] Weight resolution uses the same `aux_w_ptrs` array the K=3 forward already consumes (`f32_weight_ptrs_from_base`); indices 91-94 match the corrected trainer-side reads. The `trade_plan_activate` kernel is loaded from `EXPERIENCE_KERNELS_CUBIN` (same cubin the rest of `exp_module_extra` uses; the trainer loads it from there too). The K=3 concat now takes `exp_plan_params.raw_ptr()` instead of NULL — both sides see f(h_s2; W_plan_*_init), symmetry restored. Plan tensors at [91..94] still have no backward (no Adam updates), so the plan-head weights stay at Xavier cold-start forever. This commit delivers the symmetry the K=3 head requires, not a learned plan signal — adding a real plan-head backward is a follow-up project. New struct fields on GpuExperienceCollector: - exp_trade_plan_hidden_buf [alloc_episodes × adv_h] - exp_trade_plan_pre_out_buf [alloc_episodes × 6] - exp_plan_params [alloc_episodes × 6] - exp_trade_plan_activate_kernel: CudaFunction Lib test suite: 1016/0 green maintained. Audit doc updated. 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;