Multi-Agent Transfer Learning Based on Contrastive Role Relationship Representation

This paper presents the Multi-agent Transfer Learning Based on Contrastive Role Relationship Representation (MCRR), focusing on the unique function of role mechanisms in cross-task knowledge transfer. The framework employs contrastive learning-driven role representation modeling to capture the differences and commonalities of agent behavior patterns among multiple tasks. We generate generalizable role representations and embed them into transfer policy networks, enabling agents to efficiently share role assignment knowledge during source task training and achieve policy transfer through precise role adaptation in unseen tasks. Unlike traditional methods relying on the generalization ability of neural networks, MCRR breaks through the coordination bottleneck in multi-agent systems for dynamic team collaboration by explicitly modeling role dynamics among tasks and constructing a cross-task role contrast model. In the SMAC benchmark task series, including mixed formations and quantity variations, MCRR significantly improves win rates in both source and unseen tasks. By outperforming mainstream baselines like MATTAR and UPDeT, MCRR validates the effectiveness of roles as a bridge for knowledge transfer.