  We present our approach for the development, validation and deployment of a
data-driven decision-making function for the automated control of a vehicle.
The decisionmaking function, based on an artificial neural network is trained
to steer the mobile robot SPIDER towards a predefined, static path to a target
point while avoiding collisions with obstacles along the path. The training is
conducted by means of proximal policy optimisation (PPO), a state of the art
algorithm from the field of reinforcement learning. The resulting controller is
validated using KPIs quantifying its capability to follow a given path and its
reactivity on perceived obstacles along the path. The corresponding tests are
carried out in the training environment. Additionally, the tests shall be
performed as well in the robotics situation Gazebo and in real world scenarios.
For the latter the controller is deployed on a FPGA-based development platform,
the FRACTAL platform, and integrated into the SPIDER software stack.
