  Multiagent coordination in cooperative multiagent systems (MASs) has been
widely studied in both fixed-agent repeated interaction setting and the static
social learning framework. However, two aspects of dynamics in real-world
multiagent scenarios are currently missing in existing works. First, the
network topologies can be dynamic where agents may change their connections
through rewiring during the course of interactions. Second, the game matrix
between each pair of agents may not be static and usually not known as a prior.
Both the network dynamic and game uncertainty increase the coordination
difficulty among agents. In this paper, we consider a multiagent dynamic social
learning environment in which each agent can choose to rewire potential
partners and interact with randomly chosen neighbors in each round. We propose
an optimal rewiring strategy for agents to select most beneficial peers to
interact with for the purpose of maximizing the accumulated payoff in repeated
interactions. We empirically demonstrate the effectiveness and robustness of
our approach through comparing with benchmark strategies. The performance of
three representative learning strategies under our social learning framework
with our optimal rewiring is investigated as well.
