4709ca8bc2
Restore 45-action factored space via Branching DQN (Tavakoli 2018), outputting 11 Q-values (5+3+3) instead of 45. This was reduced to 5 exposure-only actions during debugging and was never intended as permanent. - Enable use_branching: true by default in DQNConfig and DQNHyperparameters - Add branching paths to select_action_with_confidence and select_action_inference - Update agent.rs select_action_factored for branching-aware selection - Expand CountBonus to per-branch tracking with bonuses_branched() - Add order_type + urgency distribution tracking in monitoring - Add DQN_ORDER_ACTIONS=3, DQN_URGENCY_ACTIONS=3, DQN_TOTAL_ACTIONS=45 to CUDA header - Fix 7 pre-existing clippy doc_markdown errors in regime_conditional.rs - Fix pre-existing cognitive_complexity in replay_buffer_type.rs (extract helpers) - Fix flaky GPU test OOM under parallel execution (CPU fallback + test VRAM safety) - Delete unused flash_attention submodules (block_sparse, causal_masking, etc.) - Add GPU hot-path guard scripts and ensemble/hyperopt adapter improvements Tests: ml-dqn 416/0, ml 905/0, clippy 0 errors on both crates 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;