FEMA-CL: Fair Efficient Multi-Agent Course learning

Abstract

Sociology shows that blindly pursuing the fairness of resource distribution will significantly reduce people’s enthusiasm for work, which is not conducive to the increase of total social material resources. Promoting the fairness of the social system in stages, that is, achieving fairness on the premise of a certain material basis, can not only ensure the efficiency of the total social material accumulation, but also promote the fairness of the social system. Therefore, inspired by the above, we introduced multi-stage curriculum learning into learning fair policy of multi-agent systems, and proposed a novel Fair and Effective Multi-Agent Curriculum Learning (FEMA-CL). The multi-stage course learning progressively promotes the learning fairness and efficiency of large-scale multi-agent systems through three stages: selfish stage, soft fair stage and global fair stage. Our method is easy to learn fairness and efficiency, and has carried out extensive experiments in three typical multi-agent scenarios. Compared with the current popular work, our method has superior performance.