d3a057af4f
Phase 5 plumbing — consumer-side wire-up. Allocates `aux_conf_at_state_buf: CudaSlice<f32>` ([batch_size]) on `GpuDqnTrainer`, exposes the raw_ptr via `aux_conf_at_state_buf_ptr()` and the `FusedTrainerCtx` delegating accessor `trainer_aux_conf_at_state_buf_ptr()`, and invokes `GpuReplayBuffer::set_trainer_aux_conf_ptr` at both fused_ctx init sites in training_loop.rs (init + re-init, atomic per `feedback_no_partial_refactor`). The PER `gather_f32_scalar` now writes the SAMPLED bar's per-batch aux_conf directly into the trainer's f32 buffer on every step — same direct-to-trainer pattern as the SP13 B1.1b `aux_nb_label_buf` (i32) wire-up immediately above the new call. The c51_loss_batched reward gate (lands in the next commit) reads this buffer to compute `gate = sigmoid((aux_conf - ISV[AUX_CONF_THRESHOLD]) / ISV[AUX_GATE_TEMP])` and applies `r_used = gate * reward` at the Bellman projection. `alloc_zeros` cold-start: 0.0 sentinel → at threshold ≈ 0.10 and temp ≈ 0.05 the gate is `sigmoid(-2) ≈ 0.12` → reward is mostly suppressed pre-population. This is the "graceful degradation" semantic from the Phase 3 Task 3.4 audit doc spec §4.4. Once PER's direct-gather populates from the producer ring on the first sample step, the per-bar aux_conf values drive the gate as designed. 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;