Revision as of 15:10, 14 October 2020

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.

Document type: Part of book or chapter of book

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• http://web.engr.oregonstate.edu/%7Ektumer/publications/files/tumer-yliniemi_moma-seal14.pdf