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Predicting and Preventing Coordination Problems in Cooperative Q-learning Systems
2007
International Joint Conference on Artificial Intelligence
We present a conceptual framework for creating Qlearning-based algorithms that converge to optimal equilibria in cooperative multiagent settings. This framework includes a set of conditions that are sufficient to guarantee optimal system performance. We demonstrate the efficacy of the framework by using it to analyze several well-known multi-agent learning algorithms and conclude by employing it as a design tool to construct a simple, novel multiagent learning algorithm.
dblp:conf/ijcai/FuldaV07
fatcat:433wcsenijeojf5hknsk6w7iqi