Playing Is Believing: The Role of Beliefs in Multi-Agent Learning
Abstract
We propose a new classification for multi-agent learning algorithms, with each league of players characterized by both their possible strategies and possible beliefs. Using this classification, we review the optimality of ex- isting algorithms, including the case of interleague play. We propose an incremental improvement to the existing algorithms that seems to achieve average payoffs that are at least the Nash equilibrium payoffs in the long- run against fair opponents.
Cite
Text
Chang and Kaelbling. "Playing Is Believing: The Role of Beliefs in Multi-Agent Learning." Neural Information Processing Systems, 2001.Markdown
[Chang and Kaelbling. "Playing Is Believing: The Role of Beliefs in Multi-Agent Learning." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/chang2001neurips-playing/)BibTeX
@inproceedings{chang2001neurips-playing,
title = {{Playing Is Believing: The Role of Beliefs in Multi-Agent Learning}},
author = {Chang, Yu-Han and Kaelbling, Leslie Pack},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {1483-1490},
url = {https://mlanthology.org/neurips/2001/chang2001neurips-playing/}
}