  Recent years have witnessed the great success of multi-agent systems (MAS).
Value decomposition, which decomposes joint action values into individual
action values, has been an important work in MAS. However, many value
decomposition methods ignore the coordination among different agents, leading
to the notorious "lazy agents" problem. To enhance the coordination in MAS,
this paper proposes HyperGraph CoNvolution MIX (HGCN-MIX), a method that
incorporates hypergraph convolution with value decomposition. HGCN-MIX models
agents as well as their relationships as a hypergraph, where agents are nodes
and hyperedges among nodes indicate that the corresponding agents can
coordinate to achieve larger rewards. Then, it trains a hypergraph that can
capture the collaborative relationships among agents. Leveraging the learned
hypergraph to consider how other agents' observations and actions affect their
decisions, the agents in a MAS can better coordinate. We evaluate HGCN-MIX in
the StarCraft II multi-agent challenge benchmark. The experimental results
demonstrate that HGCN-MIX can train joint policies that outperform or achieve a
similar level of performance as the current state-of-the-art techniques. We
also observe that HGCN-MIX has an even more significant improvement of
performance in the scenarios with a large amount of agents. Besides, we conduct
additional analysis to emphasize that when the hypergraph learns more
relationships, HGCN-MIX can train stronger joint policies.
