Learning When to Take Advice: A Statistical Test for Achieving a Correlated Equilibrium
Abstract
We study a multiagent learning problem where agents can either learn via repeated interactions, or can follow the advice of a mediator who suggests possible actions to take. We present an algorithm that each agent can use so that, with high probability, they can verify whether or not the mediator's advice is useful. In particular, if the mediator's advice is useful then agents will reach a correlated equilibrium, but if the mediator's advice is not useful, then agents are not harmed by using our test, and can fall back to their original learning algorithm. We then generalize our algorithm and show that in the limit it always correctly verifies the mediator's advice.
Cite
Text
Hines and Larson. "Learning When to Take Advice: A Statistical Test for Achieving a Correlated Equilibrium." Conference on Uncertainty in Artificial Intelligence, 2008.Markdown
[Hines and Larson. "Learning When to Take Advice: A Statistical Test for Achieving a Correlated Equilibrium." Conference on Uncertainty in Artificial Intelligence, 2008.](https://mlanthology.org/uai/2008/hines2008uai-learning/)BibTeX
@inproceedings{hines2008uai-learning,
title = {{Learning When to Take Advice: A Statistical Test for Achieving a Correlated Equilibrium}},
author = {Hines, Greg and Larson, Kate},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2008},
pages = {274-281},
url = {https://mlanthology.org/uai/2008/hines2008uai-learning/}
}