Subequivariant Reinforcement Learning Framework for Coordinated Motion Control
This addresses coordination challenges in motion control for complex agents, though it appears incremental as it builds on existing equivariance principles.
The paper tackles the problem of modeling intricate dependencies between joints in reinforcement learning for motion control by introducing CoordiGraph, a novel architecture that leverages subequivariant principles from physics. The result shows that CoordiGraph notably enhances generalization and sample efficiency compared to current leading methods.
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.