  The problem of autonomous racing is to navigate through a race course as
quickly as possible while not colliding with any obstacles. We approach the
autonomous racing problem with the added constraint of not maintaining an
updated obstacle map of the environment. Several current approaches to this
problem use end-to-end learning systems where an agent replaces the entire
navigation pipeline. This paper presents a hierarchical planning architecture
that combines a high level planner and path following system with a
reinforcement learning agent that learns that subsystem of obstacle avoidance.
  The novel "modification planner" uses the path follower to track the global
plan and the deep reinforcement learning agent to modify the references
generated by the path follower to avoid obstacles. Importantly, our
architecture does not require an updated obstacle map and only 10 laser range
finders to avoid obstacles. The modification planner is evaluated in the
context of F1/10th autonomous racing and compared to a end-to-end learning
baseline, the Follow the Gap Method and an optimisation based planner. The
results show that the modification planner can achieve faster average times
compared to the baseline end-to-end planner and a 94% success rate which is
similar to the baseline.
