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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 agent's 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. | 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 agent's 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. | ||
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* [http://web.engr.oregonstate.edu/%7Ektumer/publications/files/tumer-yliniemi_moma-seal14.pdf http://web.engr.oregonstate.edu/%7Ektumer/publications/files/tumer-yliniemi_moma-seal14.pdf] | * [http://web.engr.oregonstate.edu/%7Ektumer/publications/files/tumer-yliniemi_moma-seal14.pdf http://web.engr.oregonstate.edu/%7Ektumer/publications/files/tumer-yliniemi_moma-seal14.pdf] | ||
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| + | * [http://link.springer.com/content/pdf/10.1007/978-3-319-13563-2_35 http://link.springer.com/content/pdf/10.1007/978-3-319-13563-2_35], | ||
| + | : [http://dx.doi.org/10.1007/978-3-319-13563-2_35 http://dx.doi.org/10.1007/978-3-319-13563-2_35] | ||
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| + | * [http://web.engr.oregonstate.edu/~ktumer/publications/files/tumer-yliniemi_moma-seal14.pdf http://web.engr.oregonstate.edu/~ktumer/publications/files/tumer-yliniemi_moma-seal14.pdf], | ||
| + | : [https://link.springer.com/chapter/10.1007/978-3-319-13563-2_35 https://link.springer.com/chapter/10.1007/978-3-319-13563-2_35], | ||
| + | : [https://www.scipedia.com/public/Yliniemi_Tumer_2014a https://www.scipedia.com/public/Yliniemi_Tumer_2014a], | ||
| + | : [https://dblp.uni-trier.de/db/conf/seal/seal2014.html#YliniemiT14a https://dblp.uni-trier.de/db/conf/seal/seal2014.html#YliniemiT14a], | ||
| + | : [https://rd.springer.com/chapter/10.1007/978-3-319-13563-2_35 https://rd.springer.com/chapter/10.1007/978-3-319-13563-2_35], | ||
| + | : [https://academic.microsoft.com/#/detail/285701945 https://academic.microsoft.com/#/detail/285701945] | ||
Latest revision as of 17:16, 21 January 2021
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 agent's 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.
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Published on 01/01/2014
Volume 2014, 2014
DOI: 10.1007/978-3-319-13563-2_35
Licence: CC BY-NC-SA license
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