  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 and
collective reasons. However, teaching individual agents to sacrifice egoistic
benefits for a better collective performance seems challenging. We build on a
recently proposed Multi-Agent Reinforcement Learning (MARL) mechanism with a
Graph Neural Network (GNN) communication layer. Rarely chosen communication
actions were marginally beneficial. Here we propose a MARL system in which
agents can help collaborators perform better while risking low individual
performance. We conduct our study in the context of resource distribution for
wildfire management. Communicating environmental features and partially
observable fire occurrence help the agent collective to pre-emptively
distribute resources. Furthermore, we introduce a procedural training
environment accommodating auto-curricula and open-endedness towards better
generalizability. Our MA communication proposal outperforms a Greedy Heuristic
Baseline and a Single-Agent (SA) setup. We further demonstrate how
auto-curricula and openendedness improves generalizability of our MA proposal.
