In this paper, we extend the autonomous robot control and plan language RPL with constructs for specifying experiences, control tasks, learning systems and their parameterization, and exploration strategies. Using these constructs, the learning problems can be represented explicitly and transparently and become executable. With the extended language we rationally reconstruct parts of the AGILO autonomous robot soccer controllers and show the feasibility and advantages of our approach.