Multi-objective Multiagent Credit Assignment Through Difference Rewards in Reinforcement Learning

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 teambased credit assignment schema. Our results suggest that in a multiagent multiobjective problem, proper credit assignment is as important to performance as the choice of multi-objective algorithm.