论文标题
使用加固学习的多机器人形成控制
Multi-Robot Formation Control Using Reinforcement Learning
论文作者
论文摘要
在本文中，我们提出了一种机器学习方法，以在编队中移动一组机器人。我们将该问题建模为多项式增强学习问题。我们的目的是设计一种控制政策，以在朝着理想的目标迈进时保持许多代理商（机器人）之间的理想形成。这是通过训练我们的代理人跟踪该组的两个代理并保持相对于这些代理的形成来实现的。我们认为所有代理都是均匀的，并将它们建模为独轮车[1]。与领导者追随者的方法相比，每个代理都有一个独立的目标，我们的方法旨在培训代理商合作并朝着共同目标努力。我们使用这种方法的动机是制造一个完全分散的多代理编队系统，并为许多代理提供可扩展。
In this paper, we present a machine learning approach to move a group of robots in a formation. We model the problem as a multi-agent reinforcement learning problem. Our aim is to design a control policy for maintaining a desired formation among a number of agents (robots) while moving towards a desired goal. This is achieved by training our agents to track two agents of the group and maintain the formation with respect to those agents. We consider all agents to be homogeneous and model them as unicycle [1]. In contrast to the leader-follower approach, where each agent has an independent goal, our approach aims to train the agents to be cooperative and work towards the common goal. Our motivation to use this method is to make a fully decentralized multi-agent formation system and scalable for a number of agents.
