We introduce the algorithmic learning equations, a set of ordinary differential equations which characterizes the evolution of bounded memory strategies from learning algorithms. Our framework allows for a variety of information and memory structures, including noisy, perfect, private, and public monitoring and for the possibility that players use distinct learning algorithms.