12 citations · 12 across the 1 of their papers we have counts for
4 papers
Learning Fairness in Multi-Agent Systems
Jiechuan Jiang, Zongqing Lu
Fairness is essential for human society, contributing to stability and productivity. Similarly, fairness is also the key for many multi-agent systems. Taking fairness into multi-ag…
Generative Exploration and Exploitation
Jiechuan Jiang, Zongqing Lu
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we propose a novel method called Generative Exploration and Exploitation (GENE) to ove…
Graph Convolutional Reinforcement Learning
Jiechuan Jiang, Chen Dun, Tiejun Huang +1
Learning to cooperate is crucially important in multi-agent environments. The key is to understand the mutual interplay between agents. However, multi-agent environments are highly…
Learning Attentional Communication for Multi-Agent Cooperation
Jiechuan Jiang, Zongqing Lu
Communication could potentially be an effective way for multi-agent cooperation. However, information sharing among all agents or in predefined communication architectures that exi…