  Reinforcement learning (RL) has shown to be a valuable tool in training
neural networks for autonomous motion planning. The application of RL to a
specific problem is dependent on a reward signal to quantify how good or bad a
certain action is. This paper addresses the problem of reward signal design for
robotic control in the context of local planning for autonomous racing. We aim
to design reward signals that are able to perform well in multiple, competing,
continuous metrics. Three different methodologies of position-based,
velocity-based, and action-based rewards are considered and evaluated in the
context of F1/10th racing. A novel method of rewarding the agent on its state
relative to an optimal trajectory is presented. Agents are trained and tested
in simulation and the behaviors generated by the reward signals are compared to
each other on the basis of average lap time and completion rate. The results
indicate that a reward based on the distance and velocity relative to a minimum
curvature trajectory produces the fastest lap times.
