  There has recently been an increased interest in reinforcement learning for
nonlinear control problems. However standard reinforcement learning algorithms
can often struggle even on seemingly simple set-point control problems. This
paper argues that three ideas can improve reinforcement learning methods even
for highly nonlinear set-point control problems: 1) Make use of a prior
feedback controller to aid amplitude exploration. 2) Use integrated errors. 3)
Train on model ensembles. Together these ideas lead to more efficient training,
and a trained set-point controller that is more robust to modelling errors and
thus can be directly deployed to real-world nonlinear systems. The claim is
supported by experiments with a real-world nonlinear cascaded tank process and
a simulated strongly nonlinear pH-control system.
