All Learning Is Local: Multi-Agent Learning in Global Reward Games
Abstract
In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algo- rithms. We provide a simple and efficient algorithm that in part uses a linear system to model the world from a single agent’s limited per- spective, and takes advantage of Kalman filtering to allow an agent to construct a good training signal and learn an effective policy.
Cite
Text
Chang et al. "All Learning Is Local: Multi-Agent Learning in Global Reward Games." Neural Information Processing Systems, 2003.Markdown
[Chang et al. "All Learning Is Local: Multi-Agent Learning in Global Reward Games." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/chang2003neurips-all/)BibTeX
@inproceedings{chang2003neurips-all,
title = {{All Learning Is Local: Multi-Agent Learning in Global Reward Games}},
author = {Chang, Yu-han and Ho, Tracey and Kaelbling, Leslie P.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {807-814},
url = {https://mlanthology.org/neurips/2003/chang2003neurips-all/}
}