Predicting and preventing coordination problems in cooperative Q-learning systems
Nancy Fulda, Dan A. Ventura · International Joint Conference on Artificial Intelligence · 2007
We present a conceptual framework for creating Q-learning-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 multi-agent learning algorithm.