  Finding a balance between collaboration and competition is crucial for
artificial agents in many real-world applications. We investigate this using a
Multi-Agent Reinforcement Learning (MARL) setup on the back of a high-impact
problem. The accumulation and yearly growth of plastic in the ocean cause
irreparable damage to many aspects of oceanic health and the marina system. To
prevent further damage, we need to find ways to reduce macroplastics from known
plastic patches in the ocean. Here we propose a Graph Neural Network (GNN)
based communication mechanism that increases the agents' observation space. In
our custom environment, agents control a plastic collecting vessel. The
communication mechanism enables agents to develop a communication protocol
using a binary signal. While the goal of the agent collective is to clean up as
much as possible, agents are rewarded for the individual amount of
macroplastics collected. Hence agents have to learn to communicate effectively
while maintaining high individual performance. We compare our proposed
communication mechanism with a multi-agent baseline without the ability to
communicate. Results show communication enables collaboration and increases
collective performance significantly. This means agents have learned the
importance of communication and found a balance between collaboration and
competition.
