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We approach autonomous drone-based reforestation with a collaborativemulti-agent reinforcement learning (MARL) setup. Agents can communicate as partof a dynamically changing network. We explore collaboration and communicationon the back of a high-impact problem. Forests are the main resource to controlrising CO2 conditions. Unfortunately, the global forest volume is decreasing atan unprecedented rate. Many areas are too large and hard to traverse to plantnew trees. To efficiently cover as much area as possible, here we propose aGraph Neural Network (GNN) based communication mechanism that enablescollaboration. Agents can share location information on areas needingreforestation, which increases viewed area and planted tree count. We compareour proposed communication mechanism with a multi-agent baseline without theability to communicate. Results show how communication enables collaborationand increases collective performance, planting precision and the risk-takingpropensity of individual agents.

Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation