  Vital importance has necessity to be attached to cooperation in multi-agent
environments, as a result of which some reinforcement learning algorithms
combined with graph neural networks have been proposed to understand the mutual
interplay between agents. However, highly complicated and dynamic multi-agent
environments require more ingenious graph neural networks, which can
comprehensively represent not only the graph topology structure but also
evolution process of the structure due to agents emerging, disappearing and
moving. To tackle these difficulties, we propose Gumbel Sinkhorn graph
attention reinforcement learning, where a graph attention network highly
represents the underlying graph topology structure of the multi-agent
environment, and can adapt to the dynamic topology structure of graph better
with the help of Gumbel Sinkhorn network by learning latent permutations.
Empirically, simulation results show how our proposed graph reinforcement
learning methodology outperforms existing methods in the PettingZoo multi-agent
environment by learning latent permutations.
