Zero-order optimization techniques are becoming increasingly popular in robotics due to their ability to handle non-differentiable functions and escape local minima. These advantages make them particularly useful for trajectory optimization and policy optimization. In this work, we propose a mathematical tutorial on random search. It offers a simple and unifying perspective for understanding a wide range of algorithms commonly used in robotics. Leveraging this viewpoint, we classify many trajectory optimization methods under a common framework and derive novel competitive RL algorithms.

翻译：零阶优化技术因其能够处理不可微函数并逃离局部极小值而在机器人学领域日益流行。这些优势使其在轨迹优化与策略优化中尤为实用。本文提出关于随机搜索的数学教程，为理解机器人学中广泛使用的各类算法提供了简洁而统一的视角。基于这一观点，我们将多种轨迹优化方法归入统一框架进行分类，并推导出具有竞争力的新型强化学习算法。