Hao
bibliography:
- 'aaai18.bib'
title: An Optimal Rewiring Strategy for Reinforcement Social Learning in Cooperative Multiagent Systems
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Introduction
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Multiagent coordination in cooperative multiagent systems (MASs) is a significant and widely studied problem in the literature. In cooperative MASs, the agents share common interests defined by the same reward functions [@sen2007emergence]. This requires the agents to have the capability of coordinating with each other effectively towards desirable outcomes.

Until now, a lot of research efforts have been devoted to addressing multiagent coordination problems in cooperative MASs [@claus1998dynamics; @panait2005cooperative; @matignon2012independent; @lauer2000algorithm; @kapetanakis2002reinforcement; @matignon2008study]. One class of research focuses on coordination issues in fixed-agent repeated interaction settings. Claus and Boutilier firstly proposed two representative learning strategies - Independent Learner (IL) and Joint-Action Learner (JAL) to investigate their coordination performance in repeated two-agent cooperative games. Later a number of improved strategies [@lauer2000algorithm; @kapetanakis2002reinforcement; @panait2005cooperative; @matignon2008study] have been proposed to achieve coordination more efficiently and overcome the noise introduced by the high mis-coordination cost and stochasticity of games. Matignon et al. extensively investigate the comparative performance of existing independent strategies in terms of their coordination efficiency in two-agent repeated cooperative games. Their results show that perfect coordination can hardly be achieved for fully 