适应不同觅食环境的人工学习智能体群体行为的发展
Development of swarm behavior in artificial learning agents that adapt to different foraging environments
摘要：
集体行为，特别是群体的形成，已经在从生物学到物理学的各种领域中从几个角度进行了研究。在这项工作中，我们应用投影模拟将每个人建模为人工学习代理，与其邻居和周围环境进行交互，以便做出决策并从中学习。在一个强化学习框架内，我们讨论了一维学习场景，其中代理需要获得食物资源以获得奖励。我们观察到不同类型的集体运动如何出现，这取决于代理需要旅行到达资源的距离。例如，当食物源远离代理最初所在的区域时，会出现强烈对齐的群集。此外，我们还研究了不同类型的突发集体动力学中出现的个体轨迹的性质。被训练来寻找遥远资源的智能体由于集体运动而表现出具有Lévy特征的个体轨迹，而被训练来到达附近资源的智能体则表现出Brownian轨迹。
英文摘要：
Collective behavior, and swarm formation in particular, has been studied from several perspectives within a large variety of fields, ranging from biology to physics. In this work, we apply Projective Simulation to model each individual as an artificial learning agent that interacts with its neighbors and surroundings in order to make decisions and learn from them. Within a reinforcement learning framework, we discuss one-dimensional learning scenarios where agents need to get to food resources to be rewarded. We observe how different types of collective motion emerge depending on the distance the agents need to travel to reach the resources. For instance, strongly aligned swarms emerge when the food source is placed far away from the region where agents are situated initially. In addition, we study the properties of the individual trajectories that occur within the different types of emergent collective dynamics. Agents trained to find distant resources exhibit individual trajectories with Lévy-like characteristics as a consequence of the collective motion, whereas agents trained to reach nearby resources present Brownian-like trajectories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
