Meta-reinforcement learning with minimum attention
This work introduces Minimum Attention, a control-theoretic regularization framework applied to reinforcement learning and model-based meta-learning. By penalizing rapid changes in control across state and time, it stabilizes training, significantly reduces variance, and improves total rewards in tasks like locomotion. The method guides agents toward discovering structured, efficient motor skills rather than generic reactive ones.