cc96dd7fd3
Root cause of L40S segfault (train-tgdlq workflow, commit0d639dfe9): 1. F4 (IB) and F5 (barrier) gradient kernels indexed cql_d_adv_logits and on_b_logits_buf with stride b0_size (4) instead of total_actions (13). The buffer is sized [B, total_actions, num_atoms]. Writes for batch i>0 landed in other actions'/batches' gradient slots — corrupting CQL gradient for every batch beyond the first. After cuBLAS backward, weights diverged in undefined ways; validation forward then segfaulted reading NaN-laced parameters in GpuBacktestEvaluator init. Fix: add `total_actions` kernel parameter (int), use it as the full stride for adv_row and d_adv_a pointer arithmetic; keep b0_size as the loop bound (direction-branch-only update). All three launch sites updated: inlined F5 launch in apply_cql_gradient, inlined F4 launch alongside, and the standalone inject_barrier_into_cql_d_logits method. 2. D6 ensemble oracle fired at epoch 0 because per-branch Q-gaps are all 0 at random init (range = 0 → score = 1.0 → plasticity trigger). Shrink-and-perturb ran immediately, then again next epoch, etc. Gate behind `learning_health.epoch > 5` (3 warmup + 2 buffer epochs for Q to move) so the oracle only fires on post-warmup real collapse, not untrained networks. Both bugs are regressions from today's work — F4/F5 introduced yesterday, D6 became live after the ens_disagreement real-signal fix ind9d35b6fa. 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;