We present a general approach for simulating and controlling a hu- man character that is riding a bicycle. The two main components of our system are offline learning and online simulation. We sim- ulate the bicycle and the rider as an articulated rigid body system. The rider is controlled by a policy that is optimized through of- fline learning. We apply policy search to learn the optimal policies, which are parameterized with splines or neural networks for dif- ferent bicycle maneuvers. We use Neuroevolution of Augmenting Topology (NEAT) to optimize both the parametrization and the pa- rameters of our policies. The learned controllers are robust enough to withstand large perturbations and allow interactive user control. The rider not only learns to steer and to balance in normal riding sit- uations, but also learns to perform a wide variety of stunts, includ- ing wheelie, endo, bunny hop, front wheel pivot and back hop.