##基于多智能体强化学习的物理定律发现

Abstract

This paper explores a novel approach to discovering and validating fundamental physical laws using Multi-Agent Reinforcement Learning (MARL). The core idea is to leverage the interactions and competition among multiple agents within a simulated physical environment to autonomously identify and verify potential laws governing the system. Each agent is trained using a reinforcement learning algorithm, receiving rewards for behaviors that align with observed physical patterns. This approach offers a dynamic and automated method for uncovering hidden relationships and constraints, potentially leading to the discovery of new physical laws or the refinement of existing ones. The presented framework emphasizes exploration and utilizes agent interactions to drive the learning process. This research addresses the limitations of traditional physics discovery methods by introducing a system capable of adapting to complex environments and identifying patterns that might be missed by human observation. The key innovations lie in the application of MARL to this domain and the design of a reward structure that effectively guides agents toward discovering physical laws. ---