Action Principle

The action principle, a cornerstone of physics, seeks to describe system evolution by finding paths that minimize an action functional, often related to energy. Current research extends this principle beyond traditional physics, applying it to diverse fields like optimal transport (using Lagrangian costs and novel computational methods), multi-robot coordination (addressing inconsistent beliefs and decentralized planning), and generative modeling (connecting diffusion models through an action-based framework). This broadened application enhances our understanding of complex systems, offering new analytical and computational tools for problems ranging from robotics and machine learning to the analysis of narratives and the interpretation of reinforcement learning agents.