  Over the years, complex control approaches have been developed to control the
motion of a bicycle. Reinforcement Learning (RL), a branch of machine learning,
promises easy deployment of so-called agents. Deployed agents are increasingly
considered as an alternative to controllers for mechanical systems. The present
work introduces an RL approach to do path following with a virtual bicycle
model while simultaneously stabilising it laterally. The bicycle, modelled as
the Whipple benchmark model and using multibody system dynamics, has no
stabilisation aids. The agent succeeds in both path following and stabilisation
of the bicycle model exclusively by outputting steering angles, which are
converted into steering torques via a PD controller. Curriculum learning is
applied as a state-of-the-art training strategy. Different settings for the
implemented RL framework are investigated and compared to each other. The
performance of the deployed agents is evaluated using different types of paths
and measurements. The ability of the deployed agents to do path following and
stabilisation of the bicycle model travelling between 2m/s and 7m/s along
complex paths including full circles, slalom manoeuvres, and lane changes is
demonstrated. Explanatory methods for machine learning are used to analyse the
functionality of a deployed agent and link the introduced RL approach with
research in the field of bicycle dynamics.
