Multi-Agent Self-Motivated Learning via Role Representation

In collaborative multi-agent reinforcement learning (MARL), agents need to reach good collaboration in an evolving environment. The introduction of the concept of role is an effective method to recognize the complex relationship between agents and invisibly guide collaboration, but current methods make it difficult to learn excellent policies in dynamic environments. Therefore, we propose a Self-Motivated learning via Role representations (SMR) frame. Firstly, we generate compact role representations based on the trajectories of agents and achieve rational dynamic role assignments by maximizing mutual information. Second, we use an attention mechanism to make action predictions based on observations and role representations and then generate intrinsic rewards based on their uncertainty about the dynamic environment, which in turn induces implicit collaboration. We conduct experiments and visualizations on different challenging benchmark platforms, and the experimental results show the superiority of our method.