cooperative game against randomly selected opponent from its neighborhood. Empirical results show that our optimal rewiring strategy outperforms other existing benchmark strategies in terms of agents’ average accumulated payoff and robustness against different environments. Besides, the relative performance of three representative learning strategies (i.e., Fictitious Play, Joint-Action Learner and Joint-Action WoLF-PHC) is analyzed as well.

The remainder of the paper is organized as follows. In Section 2, we give an overview of related works. In Section 3, we give a formal description of our coordination problem in dynamic cooperative MASs. In Section 3, the social learning framework and both rewiring and learning strategies are described. In Section 4, we empirically demonstrate the efficiency of our rewiring approach and analyze the influence of three learning strategies under the social learning framework with rewiring. Lastly conclusion and future work are given in Section 5.

Related Work
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There has been a large amount of research in the multiagent reinforcement learning literature on solving coordination problem in cooperative MASs One line of research focuses on solving coordination problem in fixed-agent repeated interaction settings [@claus1998dynamics; @lauer2000algorithm; @kapetanakis2002reinforcement; @panait2005cooperative; @matignon2008study; @panait2006lenient; @matignon2012independent]. Claus and Boutilier investigate the coordination performance of the Independent Learner and the 