2 papers
cs.LG2025
Reducing Variance Caused by Communication in Decentralized Multi-agent Deep Reinforcement Learning
Changxi Zhu, Mehdi Dastani, Shihan Wang
In decentralized multi-agent deep reinforcement learning (MADRL), communication can help agents to gain a better understanding of the environment to better coordinate their behavio…
cs.MA2020
A Q-values Sharing Framework for Multiagent Reinforcement Learning under Budget Constraint
Changxi Zhu, Ho-fung Leung, Shuyue Hu +1
In teacher-student framework, a more experienced agent (teacher) helps accelerate the learning of another agent (student) by suggesting actions to take in certain states. In cooper…