  Model-based reinforcement learning is an effective approach for controlling
an unknown system. It is based on a longstanding pipeline familiar to the
control community in which one performs experiments on the environment to
collect a dataset, uses the resulting dataset to identify a model of the
system, and finally performs control synthesis using the identified model. As
interacting with the system may be costly and time consuming, targeted
exploration is crucial for developing an effective control-oriented model with
minimal experimentation. Motivated by this challenge, recent work has begun to
study finite sample data requirements and sample efficient algorithms for the
problem of optimal exploration in model-based reinforcement learning. However,
existing theory and algorithms are limited to model classes which are linear in
the parameters. Our work instead focuses on models with nonlinear parameter
dependencies, and presents the first finite sample analysis of an active
learning algorithm suitable for a general class of nonlinear dynamics. In
certain settings, the excess control cost of our algorithm achieves the optimal
rate, up to logarithmic factors. We validate our approach in simulation,
showcasing the advantage of active, control-oriented exploration for
controlling nonlinear systems.
