Learning to Coordinate Efficiently: A Model-based Approach
  In common-interest stochastic games all players receive an identical payoff.
Players participating in such games must learn to coordinate with each other in
order to receive the highest-possible value. A number of reinforcement learning
algorithms have been proposed for this problem, and some have been shown to
converge to good solutions in the limit. In this paper we show that using very
simple model-based algorithms, much better (i.e., polynomial) convergence rates
can be attained. Moreover, our model-based algorithms are guaranteed to
converge to the optimal value, unlike many of the existing algorithms.
