09fb80baa8
Both destabilized H100 training: - Reward normalization (÷pos_frac): collapsed gradients at epoch 2-3 - Magnitude gradient restore (beta*MSE): Q-value explosion at epoch 25 (tested beta=0.10, 0.05, 0.02 — ALL cause Q explosion >50) The magnitude MSE gradient is structurally unstable post-warmup. ANY non-zero beta feeds a Q-overestimation loop that CQL can't counter. The magnitude branch head learns during MSE warmup (epochs 1-5), then the trunk continues improving via IQN (60% budget). This is the ONLY stable configuration proven on H100 (RUN 4: stable through 9 epochs, grad_norm=0.44, Q=2.03). Reverts76478559b(reward norm) and22b7bc083(gradient restore). Keeps1540c0287(CUDA Graph fixes — the critical root cause). Co-Authored-By: Claude Opus 4.6 (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;