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 operating in a partially observable environment. We assume that the network of agents operates in a fully-decentralized manner%2C possessing the capability to exchange variables with their immediate neighbors. The proposed design methodology is supported by an analysis demonstrating that the difference between final outcomes%2C obtained when the global state is fully observed versus estimated through the social learning method%2C is %24varepsilon%24-bounded when an appropriate number of iterations of social learning updates are implemented. Unlike many existing dec-POMDP-based RL approaches%2C the proposed algorithm is suitable for model-free multi-agent reinforcement learning as it does not require knowledge of a transition model. Furthermore%2C experimental results illustrate the efficacy of the algorithm and demonstrate its superiority over the current state-of-the-art methods.?quality=80&w=800)
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