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Phase E.1 Task 10. Standalone target-augmentation kernel: m = α_m · max(τ · log π(a|s), log_clip_min) V_soft(s') = max(Q_next) + τ · log Σ exp((Q_next − max) / τ) target = r + m + γ · V_soft(s') (terminal: r + m) π(a|s) ∝ exp(Q_online(s, a) / τ) — softmax policy from the online net. Munchausen bonus is implicit KL regularisation between successive policies; soft-V replaces the hard max bootstrap with a τ-weighted softmax average. Both softmaxes are computed via log-sum-exp with the max-trick. This is essential at τ ≈ 0.03 where raw exp(Q/τ) would overflow f32 for any Q-spread > 25 nats. The kernel is one-thread-per-batch-sample, no atomicAdd, no host branches. α_m, τ, log_clip_min are kernel args (not hard-coded), so a Phase E.2+ controller can ISV-drive them. Typical Vieillard values: α_m=0.9, τ=0.03, log_clip_min=-1.0. Does NOT touch any ISV slot — pure target augmentation. Cubin: target/release/build/ml-*/out/phase_e_munchausen_target.cubin (12.8 KB). Launcher integration is Task 11 (consumes target_out where the C51/MSE loss kernels currently consume `r + γ · max_a' Q_target`). Audit doc docs/isv-slots.md updated per Invariant 7.
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;