Effective coordination is crucial for motion control with reinforcement learning, especially as the complexity of agents and their motions increases. However, many existing methods struggle to account for the intricate dependencies between joints. We introduce CoordiGraph, a novel architecture that leverages subequivariant principles from physics to enhance coordination of motion control with reinforcement learning. This method embeds the principles of equivariance as inherent patterns in the learning process under gravity influence, which aids in modeling the nuanced relationships between joints vital for motion control. Through extensive experimentation with sophisticated agents in diverse environments, we highlight the merits of our approach. Compared to current leading methods, CoordiGraph notably enhances generalization and sample efficiency.

翻译：有效的协调对于基于强化学习的运动控制至关重要，尤其是在智能体及其运动复杂度增加的情况下。然而，许多现有方法难以处理关节之间复杂的依赖关系。我们提出了CoordiGraph，这是一种新颖的架构，利用物理学中的子等变原理来增强基于强化学习的运动控制协调性。该方法将等变原理作为重力影响下学习过程中的固有模式嵌入其中，有助于建模对运动控制至关重要的关节间微妙关系。通过在多样化环境中对复杂智能体进行大量实验，我们展示了该方法的优势。与当前主流方法相比，CoordiGraph显著提升了泛化能力和样本效率。