To achieve an optimal outcome in many situations, agents need to choose
distinct actions from one another. This is the case notably in many resource
allocation problems, where a single resource can only be used by one agent at a
time. How shall a designer of a multi-agent system program its identical agents
to behave each in a different way? From a game theoretic perspective, such
situations lead to undesirable Nash equilibria. For example consider a resource
allocation game in that two players compete for an exclusive access to a single
resource. It has three Nash equilibria. The two pure-strategy NE are efficient,
but not fair. The one mixed-strategy NE is fair, but not efficient. Aumanns
notion of correlated equilibrium fixes this problem: It assumes a correlation
device that suggests each agent an action to take. However, such a "smart"
coordination device might not be available. We propose using a randomly chosen,
"stupid" integer coordination signal. "Smart" agents learn which action they
should use for each value of the coordination signal. We present a multi-agent
learning algorithm that converges in polynomial number of steps to a correlated
equilibrium of a channel allocation game, a variant of the resource allocation
game. We show that the agents learn to play for each coordination signal value
a randomly chosen pure-strategy Nash equilibrium of the game. Therefore, the
outcome is an efficient correlated equilibrium. This CE becomes more fair as
the number of the available coordination signal values increases