  In large systems, it is important for agents to learn to act effectively, but
sophisticated multi-agent learning algorithms generally do not scale. An
alternative approach is to find restricted classes of games where simple,
efficient algorithms converge. It is shown that stage learning efficiently
converges to Nash equilibria in large anonymous games if best-reply dynamics
converge. Two features are identified that improve convergence. First, rather
than making learning more difficult, more agents are actually beneficial in
many settings. Second, providing agents with statistical information about the
behavior of others can significantly reduce the number of observations needed.
