论文标题
适应不同觅食环境的人工学习代理中的群体行为的发展
Development of swarm behavior in artificial learning agents that adapt to different foraging environments
论文作者
论文摘要
从生物学到物理学的各种领域，已经从多个角度的角度研究了集体行为，尤其是群体形成。在这项工作中，我们将投影模拟应用于每个人作为人工学习的代理人，该人与邻居和周围环境互动，以做出决策并向它们学习。在强化学习框架内，我们讨论了一维学习场景，代理需要获得食物资源才能获得奖励。我们观察到不同类型的集体运动是如何根据代理商旅行以达到资源所需的距离而出现的。例如，当食物来源远离代理商最初的区域时，出现了强烈比对的群。此外，我们研究了在不同类型的新兴集体动力学中发生的单个轨迹的特性。经过培训以寻找遥远资源的特工表现出具有集体运动的带有莱维般特征的个性轨迹，而经过培训的代理商培训了到达附近的资源。
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.
