686f180d7b
Cast all Tensor::full() / Tensor::from_vec() call sites to training_dtype instead of defaulting to F32. Fixes dtype mismatch errors (BF16 vs F32) in PPO training on CUDA: - tensor_ops: scalar_mul, clamp, normalize match operand dtype - trajectories: TrajectoryBatch/MiniBatch to_tensors cast to training dtype - continuous_ppo: ContinuousTrajectoryBatch/MiniBatch to_tensors cast - adaptive_entropy: cast entropy to F32 for alpha multiplication boundary - continuous_policy: forward() input cast, Tensor::full scalars match dtype - flow_policy: sample_base_noise cast to training dtype - hidden_state_manager: reset tensors use training_dtype - ensemble/ppo adapter: predict input cast to training dtype - trainable_adapter: test uses training_dtype instead of hardcoded F32 Verified: 198/198 ml-ppo tests pass, 63/63 ml PPO tests pass. 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;