Efficient Convention Emergence through Decoupled Reinforcement Social Learning with Teacher-Student Mechanism

In this paper, we design reinforcement learning based (RLbased) strategies to promote convention emergence in multiagent systems (MASs) with large convention space. We apply our approaches to a language coordination problem in which agents need to coordinate on a dominant lexicon for efficient communication. By modeling each lexicon which maps each concept to a single word as a Markov strategy representation, the original single-state convention learning problem can be transformed into a multi-state multiagent coordination problem. The dynamics of lexicon evolutions during an interaction episode can be modeled as a Markov game, which allows agents to improve the action values of each concept separately and incrementally. Specifically we propose two learning strategies, multiple-Q and multiple-R, and also propose incorporating teacher-student mechanism on top of the learning strategies to accelerate lexicon convergence speed. Extensive experiments verify that our approaches outperform the state-of-the-art approaches in terms of convergence efficiency, convention quality and scalability.