  Coverage Path Planning involves visiting every unoccupied state in an
environment with obstacles. In this paper, we explore this problem in
environments which are initially unknown to the agent, for purposes of
simulating the task of a vacuum cleaning robot. A survey of prior work reveals
sparse effort in applying learning to solve this problem. In this paper, we
explore modeling a Cover Path Planning problem using Deep Reinforcement
Learning, and compare it with the performance of the built-in algorithm of the
Roomba, a popular vacuum cleaning robot.
