  In this paper, pragmatic implementation of an indoor autonomous delivery
system that exploits Reinforcement Learning algorithms for path planning and
collision avoidance is audited. The proposed system is a cost-efficient
approach that is implemented to facilitate a Raspberry Pi controlled
four-wheel-drive non-holonomic robot map a grid. This approach computes and
navigates the shortest path from a source key point to a destination key point
to carry out the desired delivery. Q learning and Deep-Q learning are used to
find the optimal path while avoiding collision with static obstacles. This work
defines an approach to deploy these two algorithms on a robot. A novel
algorithm to decode an array of directions into accurate movements in a certain
action space is also proposed. The procedure followed to dispatch this system
with the said requirements is described, ergo presenting our proof of concept
for indoor autonomous delivery vehicles.
