8c86325ff8
Three fixes: 1. FusedTrainingCtx::new now accepts RegimeConditionalDQN by using primary_head() — the old code rejected it, causing silent fallback to non-fused train_step() which has action-space mismatch with branching DQN (5-action Q-table vs 45-action factored indices → CUDA_ERROR_ILLEGAL_ADDRESS buffer overrun) 2. ensure_fused_ctx() returns Result instead of () — fused init failure is now a hard error (no silent CPU fallback) 3. GpuTensor::cat now supports dim>0 concatenation (was unimplemented, caused "dim=1 > 0 not yet implemented" error in validation path) Root cause chain: RegimeConditional rejected by fused init → silent fallback to DQN::train_step() → compute_loss_internal() uses num_actions=5 but actions are 0-44 → gather with out-of-bounds offsets → CUDA_ERROR_ILLEGAL_ADDRESS → async error poisons CUDA context → next stream.synchronize() deadlocks forever Remaining: curiosity kernel crash also poisons the stream. The curiosity training runs inside collect_gpu_experiences() and its async error blocks the PER insert. This needs separate investigation of curiosity_training_kernel.cu. 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;