3107edb8f7
User design call DD10 option (b): Thompson direction selector now reads
softmax(V[z] + (A[a, z] - mean_a A[*, z])) instead of pre-SP17
softmax(A[a, z]). Without V wired in, action selection responded to a
DIFFERENT distribution than compute_expected_q's E[Q] (the Bellman-target
ranking) — the very pathology SP17 is fixing at the action layer.
Architectural change closes the contract gap atomically across:
Kernel signature (`experience_action_select`):
- Added `const float* __restrict__ v_logits_dir` after `b_logits_dir`.
NULL is invalid (no fallback) — kernel hard-requires V.
- New per-thread `a_mean_per_atom_dir[THOMPSON_MAX_ATOMS]` reduction
computes mean A across the 4 direction actions per atom z (sample-local
register, no atomicAdd).
- Pass 1 (E[Q] for temperature blend + conviction): builds per-action
`combined_logits_d[z] = V[z] + (A[a,z] - mean_a A[*,z])` and feeds
`softmax_c51_inline` instead of raw `b_logits_dir + d * n_atoms`.
- Pass 2 (Thompson sample): same combined-logits rebuild per direction.
- `softmax_c51_inline` device helper UNCHANGED — keeping centering at
caller maintains a narrower contract; thompson_test_kernel and other
potential callers stay unaffected.
- THOMPSON_MAX_ATOMS=128 ceiling preserved; __trap() on overflow.
QValueProvider trait extension:
- `compute_q_and_b_logits_to` now takes `v_logits_out_ptr: u64` and
DtoD-copies `on_v_logits_buf` per sub-iteration (atomic per
feedback_no_partial_refactor — every consumer migrates in lockstep).
Trainer + evaluator wire-up:
- `gpu_dqn_trainer.rs::on_v_logits_buf_ptr() -> u64` (new pub fn, mirror
of existing `on_b_logits_buf_ptr`).
- `gpu_backtest_evaluator.rs::chunked_v_logits_buf` field allocated
[chunk_n * NA + 32*3] (cuBLAS tail-safety pad).
- Both call sites (collector + evaluator) pass v_logits arg in launch.
GPU oracle test (RTX 3050 Ti, 5/5 PASS):
thompson_direction_select_reads_v_logits — runs production cubin
twice on identical A logits with V=[0,0,0] vs V=[10,0,0]; asserts
q_gap_v_dominant < 50% of q_gap_v_zero. If V is being IGNORED
(regression), both runs produce IDENTICAL q_gaps and the test fails
with a clear message. Uses MappedF32Buffer / MappedI32Buffer per
feedback_no_htod_htoh_only_mapped_pinned.
Note on the plan's "raw argmax = Hold but centered argmax = Long" test:
Mathematical analysis shows softmax-with-constant-shift preserves
action ordering (mean subtraction adds the same per-atom constant to
every action's logits), so the plan's specific assertion isn't
algebraically constructable with simple A/V. The replacement test
(V-dependence of q_gap) is more sensitive — it fails on the actual
regression case (V ignored ⇒ identical q_gaps) the plan was trying
to detect.
Verification:
cargo check --workspace → clean
cargo test sp17_dueling_oracle_tests --features cuda
-- --ignored → 5/5 PASS
⚠ INTERIM STATE: aux-CQL barrier_gradient_direction +
ib_gradient_direction still read raw advantage. Commit D closes them;
Commit E annotates the pre-SP17 c51_loss/c51_grad already-centered sites.
Plan: docs/superpowers/plans/2026-05-08-sp17-dueling-q-network.md
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;