Aspects of organizing game interactions among asymmetric agents using graph neural networks

The article considers the structures of representation of the graph of inter-agent connections for increasing the efficiency of agent interaction in cooperative competitive games using graph neural networks. A comparative assessment of metrics and adjacency matrices for graphs of connections defined using geometric and semantic metrics of proximity is performed. It is shown that semantic proximity is more effective in constructing a graph of inter-agent connections, and the use of oriented graphs ensures flexible management of information flows. The proposed patterns are important to consider when organizing multi-agent reinforcement learning in a wide range of application areas.