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Multiagent systems have had a powerful impact on the real world. Many of the systems it studies (air traffic, satellite coordination, rover exploration) are inherently multi-objective, but they are often treated as single-objective problems within the research. A very important concept within multiagent systems is that of credit assignment: clearly quantifying an individual agentŐs impact on the overall system performance. In this work we extend the concept of credit assignment into multi-objective problems, broadening the traditional multiagent learning framework to account for multiple objectives. We show in two domains that by leveraging established credit assignment principles in a multi-objective setting, we can improve performance by (i) increasing learning speed by up to 10x; (ii) reducing sensitivity to unmodeled disturbances by up to 98.4%; and (iii) producing solutions that dominate all solutions discovered by a traditional team- based credit assignment schema. Our results suggest that in a multiagent multi- objective problem, proper credit assignment is as important to performance as the choice of multi-objective algorithm.
@inproceedings{tumer-yliniemi_moma-seal14,
author = {L. Yliniemi and K. Tumer},
title = {Multi-Objective Multiagent Credit Assignment Through Difference Rewards in Reinforcement Learning},
booktitle = {The Tenth International Conference on Simulated Evolution And Learning (SEAL 2014)},
month = {December},
address = {Dunedin, New Zealand},
abstract = {Multiagent systems have had a powerful impact on the real world. Many of the systems it studies (air traffic, satellite coordination, rover exploration) are inherently multi-objective, but they are often treated as single-objective problems within the research. A very important concept within multiagent systems is that of credit assignment: clearly quantifying an individual agentÕs impact on the overall system performance. In this work we extend the concept of credit assignment into multi-objective problems, broadening the traditional multiagent learning framework to account for multiple objectives. We show in two domains that by leveraging established credit assignment principles in a multi-objective setting, we can improve performance by (i) increasing learning speed by up to 10x; (ii) reducing sensitivity to unmodeled disturbances by up to 98.4%; and (iii) producing solutions that dominate all solutions discovered by a traditional team- based credit assignment schema. Our results suggest that in a multiagent multi- objective problem, proper credit assignment is as important to performance as the choice of multi-objective algorithm.},
bib2html_pubtype = {Refereed Conference Papers},
bib2html_rescat = {Multiagent Systems, Optimization},
year = {2014}
}
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