39 citations · 60 across the 3 of their papers we have counts for
5 papers
Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing
Hangyu Mao, Zhibo Gong, Zhen Xiao
In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem. The global…
Learning Agent Communication under Limited Bandwidth by Message Pruning
Hangyu Mao, Zhengchao Zhang, Zhen Xiao +2
Communication is a crucial factor for the big multi-agent world to stay organized and productive. Recently, Deep Reinforcement Learning (DRL) has been applied to learn the communic…
Learning Multi-agent Communication under Limited-bandwidth Restriction for Internet Packet Routing
Hangyu Mao, Zhibo Gong, Zhengchao Zhang +2
Communication is an important factor for the big multi-agent world to stay organized and productive. Recently, the AI community has applied the Deep Reinforcement Learning (DRL) to…
Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG
Hangyu Mao, Zhengchao Zhang, Zhen Xiao +1
Modelling and exploiting teammates' policies in cooperative multi-agent systems have long been an interest and also a big challenge for the reinforcement learning (RL) community. T…
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning
Hangyu Mao, Zhibo Gong, Yan Ni +1
Communication is a critical factor for the big multi-agent world to stay organized and productive. Typically, most previous multi-agent "learning-to-communicate" studies try to pre…