  Single-Agent (SA) Reinforcement Learning systems have shown outstanding
re-sults on non-stationary problems. However, Multi-Agent Reinforcement
Learning(MARL) can surpass SA systems generally and when scaling. Furthermore,
MAsystems can be super-powered by collaboration, which can happen through
ob-serving others, or a communication system used to share information
betweencollaborators. Here, we developed a distributed MA learning mechanism
withthe ability to communicate based on decentralised partially observable
Markovdecision processes (Dec-POMDPs) and Graph Neural Networks (GNNs).
Minimis-ing the time and energy consumed by training Machine Learning models
whileimproving performance can be achieved by collaborative MA mechanisms.
Wedemonstrate this in a real-world scenario, an offshore wind farm, including a
set ofdistributed wind turbines, where the objective is to maximise collective
efficiency.Compared to a SA system, MA collaboration has shown significantly
reducedtraining time and higher cumulative rewards in unseen and scaled
scenarios.
