a24c3d2ee0
Three critical fixes for the 6/45 action diversity collapse and RegimeConditional regime routing: 1. DQNAgentType::forward() for RegimeConditional now delegates to batch_q_values() which uses GPU regime classification masks to blend all 3 heads (trending/ranging/volatile). Previously hard-coded to trending head only — ranging/volatile heads were trained but never used during experience collection. 2. New batch_branching_q_values() on RegimeConditionalDQN: returns per-branch (exposure/order/urgency) Q-values blended across regime heads via GPU masks. Enables the GPU action selector's select_actions_branching() for per-branch epsilon-greedy. 3. select_actions_batch() and select_actions_batch_gpu() now support branching DQN for both Standard and RegimeConditional agents. ROOT CAUSE FIX: previously used exposure-only Q-values (0-4) with deterministic route_action(), limiting diversity to 6/45 actions. Now uses per-branch Q-values with independent epsilon per branch, enabling full 45-action exploration. Tests: ml-dqn=416, ml=915, 0 failures 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;