Display Publications by [Year] [Type] [Topic]
In this paper we test two coordination methods -- difference rewards and coordination graphs -- in a continuous, multiagent rover domain using reinforcement learning, and discuss the situations in which each of these methods perform better alone or together, and why. We also contribute a novel method of applying coordination graphs in a continuous domain by taking advantage of the wire-fitting approach used to handle continuous state and action spaces.
@inproceedings{tumer-proper_aamas13,
author = {S. Proper and K. Tumer},
title = {Graphical Models in Continuous Domains for Multiagent Reinforcement Learning (extended abstract)},
booktitle = {Proceedings of the Twelveth International Joint Conference on Autonomous Agents and Multiagent Systems},
month = {May},
address = {Minneapolis, MN},
abstract={In this paper we test two coordination methods -- difference rewards and coordination graphs -- in a continuous, multiagent rover domain using reinforcement learning, and discuss the situations in which each of these methods perform better alone or together, and why. We also contribute a novel method of applying coordination graphs in a continuous domain by taking advantage of the wire-fitting approach used to handle continuous state and action spaces.},
bib2html_pubtype = {Refereed Conference Papers},
bib2html_rescat = {Multiagent Systems, Reinforcement Learning},
year = {2013}
}
Generated by bib2html.pl (written by Patrick Riley ) on Wed Apr 01, 2020 17:39:43