  The application of deep reinforcement learning in multi-agent systems
introduces extra challenges. In a scenario with numerous agents, one of the
most important concerns currently being addressed is how to develop sufficient
collaboration between diverse agents. To address this problem, we consider the
form of agent interaction based on neighborhood and propose a multi-agent
reinforcement learning (MARL) algorithm based on the actor-critic method, which
can adaptively construct the hypergraph structure representing the agent
interaction and further implement effective information extraction and
representation learning through hypergraph convolution networks, leading to
effective cooperation. Based on different hypergraph generation methods, we
present two variants: Actor Hypergraph Convolutional Critic Network (HGAC) and
Actor Attention Hypergraph Critic Network (ATT-HGAC). Experiments with
different settings demonstrate the advantages of our approach over other
existing methods.
