Minimum attention applies the least action principle in the changes of control concerning state and time, first proposed by Brockett. The involved regularization is highly relevant in emulating biological control, such as motor learning. We apply minimum attention in reinforcement learning (RL) as part of the rewards and investigate its connection to meta-learning and stabilization. Specifically, model-based meta-learning with minimum attention is explored in high-dimensional nonlinear dynamics. Ensemble-based model learning and gradient-based meta-policy learning are alternately performed. Empirically, the minimum attention does show outperforming competence in comparison to the state-of-the-art algorithms of model-free and model-based RL, i.e., fast adaptation in few shots and variance reduction from the perturbations of the model and environment. Furthermore, the minimum attention demonstrates an improvement in energy efficiency.

翻译：最小注意力将Brockett首次提出的最小作用量原理应用于控制中关于状态和时间的动态变化。所涉及的正则化在模拟生物控制（如运动学习）中具有高度相关性。我们将最小注意力作为奖励函数的一部分应用于强化学习（RL），并研究其与元学习和稳定性之间的关系。具体而言，我们在高维非线性动力学系统中探索了基于模型且融入最小注意力的元学习方法。该方法交替执行基于集成策略的模型学习和基于梯度的元策略学习。实验表明，与当前最先进的无模型和基于模型的强化学习算法相比，最小注意力确实展现出更优越的性能，具体体现在：在少量样本中实现快速适应，并有效降低模型与环境扰动带来的方差。此外，最小注意力还表现出能量效率的提升。