Abstract
This paper proposes a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for a team of Unmanned Aerial Vehicles (UAVs). The proposed MARL algorithm allows UAVs to learn cooperatively to provide a full coverage of an unknown field of interest while minimizing the overlapping sections among their field of views. Two challenges in MARL for such a system are discussed in the paper: firstly, the complex dynamic of the joint-actions of the UAV team, that will be solved using game-theoretic correlated equilibrium, and secondly, the challenge in huge dimensional state space representation will be tackled with efficient function approximation techniques. We also provide our experimental results in detail with both simulation and physical implementation to show that the UAV team can successfully learn to accomplish the task.
Abstract (translated)
本文提出了一种用于无人机（UAV）团队的分布式多智能体强化学习（MARL）算法。所提出的MARL算法允许UAV协同学习以提供对未知感兴趣场的完全覆盖，同时最小化其视场之间的重叠部分。本文讨论了MARL对这种系统的两个挑战：首先，无人机团队联合行动的复杂动态，将通过博弈论相关均衡来解决，其次是巨大维状态空间的挑战。将使用有效的函数逼近技术来处理表示。我们还通过仿真和物理实现详细提供了我们的实验结果，以表明无人机团队可以成功地学习完成任务。
URL
https://arxiv.org/abs/1803.07250
