  Stemming on the idea that a key objective in reinforcement learning is to
invert a target distribution of effects, end-effect drives are proposed as an
effective way to implement goal-directed motor learning, in the absence of an
explicit forward model. An end-effect model relies on a simple statistical
recording of the effect of the current policy, here used as a substitute for
the more resource-demanding forward models. When combined with a reward
structure, it forms the core of a lightweight variational free energy
minimization setup. The main difficulty lies in the maintenance of this
simplified effect model together with the online update of the policy. When the
prior target distribution is uniform, it provides a ways to learn an efficient
exploration policy, consistently with the intrinsic curiosity principles. When
combined with an extrinsic reward, our approach is finally shown to provide a
faster training than traditional off-policy techniques.
