  Learning in games has been widely used to solve many cooperative multi-agent
problems such as coverage control, consensus, self-reconfiguration or
vehicle-target assignment. One standard approach in this domain is to formulate
the problem as a potential game and to use an algorithm such as log-linear
learning to achieve the stochastic stability of globally optimal
configurations. Standard versions of such learning algorithms are asynchronous,
i.e., only one agent updates its action at each round of the learning process.
To enable faster learning, we propose a synchronization strategy based on
decentralized random prioritization of agents, which allows multiple agents to
change their actions simultaneously when they do not affect each other's
utility or feasible actions. We show that the proposed approach can be
integrated into any standard asynchronous learning algorithm to improve the
convergence speed while maintaining the limiting behavior (e.g., stochastically
stable configurations). We support our theoretical results with simulations in
a coverage control scenario.
