Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization

Hoi-To Wai; Zhuoran Yang; Zhaoran Wang; Mingyi Hong

NeurIPS 2018poster212 citations

Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization

Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong

Abstract

Despite the success of single-agent reinforcement learning, multi-agent reinforcement learning (MARL) remains challenging due to complex interactions between agents. Motivated by decentralized applications such as sensor networks, swarm robotics, and power grids, we study policy evaluation in MARL, where agents with jointly observed state-action pairs and private local rewards collaborate to learn the value of a given policy.

BibTeX

@inproceedings{NEURIPS2018_5a378f84,
 author = {Wai, Hoi-To and Yang, Zhuoran and Wang, Zhaoran and Hong, Mingyi},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization},
 url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/5a378f8490c8d6af8647a753812f6e31-Paper.pdf},
 volume = {31},
 year = {2018}
}