jgrusewski
a5ea8713b6
feat(dqn): Branching DQN + KernelWeightPack + metrics propagation
Branching DQN (Tavakoli et al., 2018):
- 3 independent advantage heads (exposure=5, order=3, urgency=3) in CUDA kernel
- `use_branching` as hyperopt-tunable parameter (index 11 in 29D space)
- Branch weight pointers packed into KernelWeightPack struct
KernelWeightPack refactor:
- 87→38 CUDA kernel params by packing 48 weight pointers into 384-byte #[repr(C)] struct
- UNPACK_WEIGHT_PTRS macro in CUDA header for clean kernel-side access
- Single .arg(&weight_pack) replaces 48 individual .arg() calls
Hyperopt metrics in JSON output:
- Added `metrics: Option<serde_json::Value>` to TrialResult<P>
- `extract_metrics()` trait method with default None (DQN overrides)
- JSON output now includes per-trial backtest metrics (Sharpe, Sortino,
Calmar, Omega, drawdown, win_rate, trades) + top-level best_metrics
Prometheus backtest gauges (Rust-side, no CUDA):
- 8 new gauges: foxhunt_hyperopt_best_{sharpe,sortino,calmar,omega,
max_drawdown_pct,win_rate,total_trades,total_return_pct}
Dynamic episode scaling:
- Removed hardcoded 256 episode cap on small GPUs
- VRAM budget calculation in optimal_n_episodes() handles scaling naturally
- Removed stale .min(256) in PPO trainer
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-11 00:46:53 +01:00
..
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