Multi-agent cooperation based on reinforcement learning
Dongyong Yang · Journal of Zhejiang University of Technology · 2004
Reinforcement learning based on Markov decision process is a way of on-line learning, which can be applied to single agent environment. However, due to the theoretical limitation that it assumes that an environment is Markovian, traditional reinforcement learning algorithms cannot be applied directly to multi-agent system. In this paper, a two-layer reinforcement learning method for multi-agent cooperation is presented. The proposed method is realized by adding two-layer reinforcement learning units to every agent. The first layer is for learning global cooperation strategy, and the second layer is for learning efficient action policy in one's own view. An experiment that three agents raise a disk-like object cooperatively has been done. Results show that the cooperative performance with the presented method is better than that using traditional reinforcement learning.