基于多智能体强化学习的紧急逃离群集行为
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning
摘要：
在自然界中，在许多物种中观察到群集或群集行为，因为它具有有益的特性，例如减少被捕食者捕获的可能性。在本文中，我们提出了SELFish（Swarm Emergent Learning Fish），一种具有多个自主智能体的方法，这些智能体可以在连续空间中自由移动，以避免被当前捕食者捕获。捕食者有一种特性，它可能会被附近的多个可能的猎物分散注意力。我们表明，这种属性在与自我利益的代理人进行交互，这些代理人经过强化学习训练，尽可能长时间地生存，从而导致类似于Boids的群集行为，这是群集行为的常见模拟。此外，我们提出了有趣的见解，在群集行为，并在我们的建模环境中被抓获的过程中的代理。
英文摘要：
In nature, flocking or swarm behavior is observed in many species as it has beneficial properties like reducing the probability of being caught by a predator. In this paper, we propose SELFish (Swarm Emergent Learning Fish), an approach with multiple autonomous agents which can freely move in a continuous space with the objective to avoid being caught by a present predator. The predator has the property that it might get distracted by multiple possible preys in its vicinity. We show that this property in interaction with self-interested agents which are trained with reinforcement learning to solely survive as long as possible leads to flocking behavior similar to Boids, a common simulation for flocking behavior. Furthermore we present interesting insights in the swarming behavior and in the process of agents being caught in our modeled environment.
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。
