feat: TOML training profile system — config-driven hyperparameters
Training Profile Loader:
- 3-tier resolution: $FOXHUNT_TRAINING_PROFILE > filesystem > embedded defaults
- DqnTrainingProfile with 10 sections, all Option<T> for sparse profiles
- apply_to() applies only Some fields, preserving struct defaults
- 11 unit tests, all passing
TOML Profiles (config/training/):
- dqn-production.toml: full Rainbow DQN (40+ params)
- dqn-smoketest.toml: CI fast path (sparse, 8 overrides)
- dqn-hyperopt.toml: PSO search space ranges + fixed flags
- ppo-production.toml, ppo-smoketest.toml
- supervised-production.toml, supervised-smoketest.toml
- walk-forward.toml: window sizes
CLI Integration:
- train_baseline_rl: --training-profile (default: dqn-production)
- train_baseline_supervised: --training-profile (default: supervised-production)
- Merge priority: CLI args > TOML profile > GPU profile > struct defaults
Smoke Tests:
- smoke_params() now loads dqn-smoketest.toml instead of hardcoding
- Production features set as manual overrides (testing flags, not config)
Infrastructure:
- K8s job-template.yaml: TRAINING_PROFILE env var + --training-profile arg
- Delete old config/ml/training.toml (replaced, zero callers)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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