|
|
|
|
|
• By Date • By Publication Type • By Research Category • By Author (Currently at CMU) • By Author (Past and Current CORAL Contributors) •
Sonia Chernova and Manuela Veloso. Multiagent Collaborative Task Learning through Imitation. In 4th International Symposium on Imitation in Animals and Artifacts, Newcastle, UK, April 2007.
Learning through imitation is a powerful approach for acquiring new behaviors. Imitation-based methods have been successfully applied to a wide range of single agent problems, consistently demonstrating faster learning rates compared to exploration-based approaches such as reinforcement learning. The potential for rapid behavior acquisition from human demonstration makes imitation a promising approach for learning in multiagent systems. In this work, we present results from our single agent demonstration-based learning algorithm, aimed at reducing demonstration demand of a single agent on the teacher over time. We then demonstrate how this approach can be applied to effectively train a complex multiagent task requiring explicit coordination between agents. We believe that this is the first application of demonstration-based learning to simultaneously training distinct policies to multiple agents. We validate our approach with experiments in two complex simulated domains.
@inproceedings{Chernova07aisb,
title="Multiagent Collaborative Task Learning through Imitation",
author="Sonia Chernova and Manuela Veloso",
booktitle="4th International Symposium on Imitation in Animals and Artifacts",
place="Newcastle upon Tyne, UK", month="April",
year="2007",
abstract={Learning through imitation is a powerful approach for acquiring new behaviors. Imitation-based methods have been successfully
applied to a wide range of single agent problems, consistently demonstrating faster learning rates compared to exploration-based approaches
such as reinforcement learning. The potential for rapid behavior acquisition from human demonstration makes imitation a promising approach
for learning in multiagent systems. In this work, we present results from our single agent demonstration-based learning algorithm, aimed at
reducing demonstration demand of a single agent on the teacher over time. We then demonstrate how this approach can be applied to effectively
train a complex multiagent task requiring explicit coordination between agents. We believe that this is the first application of
demonstration-based learning to simultaneously training distinct policies to multiple agents. We validate our approach with experiments in
two complex simulated domains.},
bib2html_pubtype = {Workshop},
bib2html_rescat = {Learning from Demonstration,Multi-Agent Systems, Multiagent Learning},
}
Generated by bib2html.pl (written by Patrick Riley ) on Tue Oct 09, 2007 00:00:13
|
|
 | |||
| [home] [news] [people] [robosoccer] [projects] [multimedia] [download] [publications] [contact us] | |||