stochastic games. However, in practical complex environments, the interactions between agents can be sparse, i.e., it is highly likely that each agent may not have global access and only have the opportunity to interact with local neighbors. Moreover, agent’s interacting partners are not fixed and may change frequently and randomly. For example, in social media [@ellison2007social], it is commonly accepted that the user attention span is limited, hence it is of great importance to identify interactions that have optimal rewards. Thereby, another line of research focuses on investigating the question of how a population of cooperative agents can effectively coordinate among each other under the social learning framework [@sen2007emergence; @villatoro2011social; @yu2013emergence; @hao2013dynamics; @hao2014reinforcement; @hao2017dynamics; @airiau2014emergence; @mihaylov2014decentralized]. For example, Hao and Leung extend IL and JAL into the social learning framework and find that better coordination performance can be achieved by leveraging the observation mechanism.

Most of existing works under the social learning framework assume that agents are located in a static network. During each round of interaction, each agent plays the same cooperative game with a randomly selected partners from its neighborhood. However, two important aspects of dynamics in real-world multiagent scenarios are currently missing. First, the cooperative games might 