Reviews: Learning Fairness in Multi-Agent Systems

The authors propose a Fair-Efficient Network to better to train decentralized multi-agent reinforcement learning systems in tasks that involve resource allocation. In particular they introduce a shaping reward and a hierarchical model which they train with PPO on three new reinforcement learning environments (the code of which is made available). Their model outperforms several baselines, and ablation studies demonstrate the usefulness of the hierarchical nature of the model. The aims of the work are clear and well-stated. However, there are significant omissions in the review of related literature.