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Most real-world domains can be formulated as multi-agent (MA) systems.Intentionality sharing agents can solve more complex tasks by collaborating,possibly in less time. True cooperative actions are beneficial for egoistic andcollective reasons. However, teaching individual agents to sacrifice egoisticbenefits for a better collective performance seems challenging. We build on arecently proposed Multi-Agent Reinforcement Learning (MARL) mechanism with aGraph Neural Network (GNN) communication layer. Rarely chosen communicationactions were marginally beneficial. Here we propose a MARL system in whichagents can help collaborators perform better while risking low individualperformance. We conduct our study in the context of resource distribution forwildfire management. Communicating environmental features and partiallyobservable fire occurrence help the agent collective to pre-emptivelydistribute resources. Furthermore, we introduce a procedural trainingenvironment accommodating auto-curricula and open-endedness towards bettergeneralizability. Our MA communication proposal outperforms a Greedy HeuristicBaseline and a Single-Agent (SA) setup. We further demonstrate howauto-curricula and openendedness improves generalizability of our MA proposal.

Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with  Graph Neural Network Communication Layer for Open-ended Wildfire-Management  Resource Distribution