Fixes Q-value collapse via unified LearningHealth signal that senses
training health (6 components) and continuously adapts:
- CQL regularization (regime + health gated)
- Gradient budget (IQN/CQL/Ens/C51 dynamic allocation)
- Tau target EMA (health-coupled)
- Expected SARSA temperature (continuous, no hardcoded threshold)
Plus 4 pearls (PER priorities, spectral detection, gradient consistency,
adaptive gamma) and 8 novels (self-distillation, barrier loss, plasticity
injection, CF curriculum, information bottleneck, ensemble oracle,
contrarian override, meta-Q network).
Core principle: training hyperparameters are OUTPUTS of the temporal
pipeline, not static schedules. The system meta-learns its own settings.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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