论文标题
动态的协作多代理增强学习沟通，用于自动无人机造林
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation
论文作者
论文摘要
我们通过协作多代理增强学习（MARL）设置来处理基于自动无人机的重新造林。代理可以作为动态变化网络的一部分进行通信。我们探索了高影响问题的背面的协作和沟通。森林是控制二氧化碳条件上升的主要资源。不幸的是，全球森林量正在以前所未有的速度下降。许多地区太大，难以穿越而无法种植新树木。为了有效地覆盖尽可能多的领域，我们在这里提出了一个基于图形神经网络（GNN）的通信机制，以实现协作。代理商可以共享有关需要造林的区域的位置信息，这些信息可以增加观察区域和种植树木的数量。我们将我们提出的沟通机制与多代理基线进行比较，没有沟通能力。结果表明，沟通如何实现协作并提高集体绩效，播种精度和个人代理的冒险倾向。
We approach autonomous drone-based reforestation with a collaborative multi-agent reinforcement learning (MARL) setup. Agents can communicate as part of a dynamically changing network. We explore collaboration and communication on the back of a high-impact problem. Forests are the main resource to control rising CO2 conditions. Unfortunately, the global forest volume is decreasing at an unprecedented rate. Many areas are too large and hard to traverse to plant new trees. To efficiently cover as much area as possible, here we propose a Graph Neural Network (GNN) based communication mechanism that enables collaboration. Agents can share location information on areas needing reforestation, which increases viewed area and planted tree count. We compare our proposed communication mechanism with a multi-agent baseline without the ability to communicate. Results show how communication enables collaboration and increases collective performance, planting precision and the risk-taking propensity of individual agents.
