809295dc5f
Three bugs in the GPU experience collection hot path: 1. gpu_batch_to_experiences() had hardcoded state_dim=43 but the CUDA kernel outputs states at the ALIGNED dimension (56 with OFI, 48 without). After sample 0, every replay buffer entry had corrupted state vectors — the network was learning from garbage data. 2. GPU path never called monitor.track_reward(), so mean_reward was always reported as 0.0 in epoch logs despite the agent generating real rewards. 3. Action tracking was double-counted (direct array write + track_action_by_exposure), inflating diversity metrics by 2x. Consolidated into single bounded call. Also adds missing app.kubernetes.io/component label to job-template.yaml so Prometheus training-pods scrape job discovers training pods. Co-Authored-By: Claude Opus 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;