The Impact of Exploration on Convergence and Performance of Multi-Agent Q-Learning Dynamics

Understanding the impact of exploration on the behaviour of multi-agent learning has, so far, benefited from the restriction to potential, or network zero-sum games in which convergence to an equilibrium can be shown. Outside of these classes, learning dynamics rarely converge and little is known about the effect of exploration in the face of non-convergence. To progress this front, we study the smooth Q- Learning dynamics. We show that, in any network game, exploration by agents results in the convergence of Q-Learning to a neighbourhood of an equilibrium. This holds independently of whether the dynamics reach the equilibrium or display complex behaviours. We show that increasing the exploration rate decreases the size of this neighbourhood and also decreases the ability of all agents to improve their payoffs. Furthermore, in a broad class of games, the payoff performance of Q-Learning dynamics, measured by Social Welfare, decreases when the exploration rate increases. Our experiments show this to be a general phenomenon, namely that exploration leads to improved convergence of Q-Learning, at the cost of payoff performance.