Beyond Single Roles: Structured Role Discovery for Cooperative Multi-Agent Reinforcement Learning

Role-based multi-agent reinforcement learning (MARL) learns roles to organize agents into specialized divisions of labor. Effective coordination requires balancing knowledge sharing with behavioral diversity. However, a central challenge is to move beyond current role assignments toward persistent role structures that capture inter-role associations and support reuse across episodes. To address this challenge, we propose SPAR, which constructs a role prototype space using semantic slots and represents each agent through a dual semantic assignment consisting of a primary role and an auxiliary role. Building on this representation, SPAR employs relation-preserving contrastive learning to optimize the geometric structure of the role space and introduces a performance-guided slot memory to reuse high-quality collaboration patterns. Experiments on SMAC and SMACv2 demonstrate that SPAR can learn dynamic yet structured roles and achieves competitive performance across multiple test scenarios.