4189da563f
Tasks 13-16 of Plan 1 §4.C.6 — Batch B controller migrations: - Task 13 (TauController): cosine-annealed Polyak EMA coefficient with health-coupled floor. Mirrors compute_cosine_annealed_tau + apply_health_coupled_tau_floor from fused_training.rs/gpu_dqn_trainer.rs. Wired at epoch-boundary B2/G3 block in training_loop.rs; per-step actuators (set_tau_host, target_ema_update) remain in fused_training.rs. 8 unit tests. - Task 14 (EpsilonController): ISV-adaptive exploration epsilon. Replaces inline base_floor*(0.5+volatility) block (~10 lines) in initialize_epoch_state. Volatility from ISV[2]; agent.set_epsilon() actuator call preserved. 8 unit tests. - Task 15 (ConvictionFloorController): IQL branch_scales per-sample floor scaffolding. Static 0.1 (SchemaContract slot ISV[36]). write_output writes to ISV[36]. Never fires. Plan 3 adds ramp logic. 7 unit tests. - Task 16 (PlanThresholdController): plan activation threshold scaffolding. Static 0.5, mirrors the > 0.5f literal in experience_kernels.cu and backtest_plan_kernel.cu. Never fires. Plan 3 §B.4 wires ISV slot. 6 unit tests. All 29 new tests pass. Cargo check: 8 warnings (unchanged from baseline). Audit doc updated per Invariant 7. 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;