Multiagent Q-Learning: Preliminary Study on Dominance between the Nash and

Some game theory approaches to solve multiagent reinforce-ment learning in self play, i.e. when agents use the same al-gorithm for choosing action, employ equilibriums, such as the Nash equilibrium, to compute the policies of the agents. These approaches have been applied only on simple exam-ples. In this paper, we present an extended version of Nash Q-Learning using the Stackelberg equilibrium to address a wider range of games than with the Nash Q-Learning. We show that mixing the Nash and Stackelberg equilibriums can lead to better rewards not only in static games but also in stochastic games. Moreover, we apply the algorithm to a real world example, the automated vehicle coordination problem