AI 中文总结
提出通过强化学习训练stigmergic智能体控制蚁群模型的框架，智能体在集中训练分散执行设置中优化，通过信息素场与蚂蚁互动，奖励设计促进特定结构，结果表明策略能移动相变线，为复杂系统控制提供见解。
AI 中文摘要
在这项工作中，我们为蚁群模型的功能可控性提出了一个新颖的框架，蚁群模型是集体行为中一个著名且相关的模型。我们的方法引入了一群通过强化学习训练的控制stigmergic智能体，它们作用于环境以影响系统动态并促进有序行为的出现。stigmergic智能体在集中训练分散执行的设置中进行优化，仅通过共享的信息素场与蚂蚁相互作用。奖励设计促进轨迹信息素结构以及蚂蚁位置与高信息素路径的对齐，而无需控制特定的微观配置。我们的结果表明，学习到的策略有效地移动了表征系统全局行为的相变线，使得在通常由随机性主导的区域中出现轨迹场景。本研究为基于强化学习的复杂系统控制策略的潜力提供了见解，有助于该领域对功能可控性的一般理解。
英文摘要
In this work, we propose a novel framework for the functional controllability of the ant swarm model, a well-known and relevant model of collective behaviour. Our approach introduces a population of controlling stigmergic agents, trained via Reinforcement Learning (RL), that act on the environment to influence the system dynamics and promote the emergence of ordered behaviour. Stigmergic agents are optimized in a centralized-training decentralized-execution setting, interacting with ants only through the shared pheromone field. The reward design promotes trail pheromone structures and alignment of ant positions with high-pheromone paths, without requiring control of specific microscopic configurations. Our results demonstrate that the learned policies effectively shift the phase transition line that characterizes the global behaviour of the system, enabling the emergence of trails scenarios in regimes that are typically dominated by randomness. This study provides insights into the potential of RL based control strategies for complex systems, contributing to the general understanding of functional controllability in this field.
