This paper examines the convergence properties of a class of learning schemes for concave N -person games – that is, games with convex action spaces and individually concave payoff functions. Specifically, we focus on a family of learning methods where players adjust their actions by taking small steps along their individual payoff gradients and then “mirror” the output back to their feasible a...