  Modern day computer games have extremely large state and action spaces. To
detect bugs in these games' models, human testers play the games repeatedly to
explore the game and find errors in the games. Such gameplay is exhaustive and
time consuming. Moreover, since robotics simulators depend on similar methods
of model specification and debugging, the problem of finding errors in the
model is of interest to the robotics community to ensure robot behaviors and
interactions are consistent in simulators. Previous methods have used
reinforcement learning arXiv:2103.13798 and search based methods (Chang, 2019,
(Chang, 2021) arXiv:1811.06962 including Rapidly-exploring Random Trees (RRT)
to explore a game's state-action space to find bugs. However, such search and
exploration based methods are not efficient at exploring the state-action space
without a pre-defined heuristic. In this work we attempt to combine a
human-tester's expertise in solving games, and the RRT's exhaustiveness to
search a game's state space efficiently with high coverage. This paper
introduces Cloning Assisted RRT (CA-RRT) to test a game through search. We
compare our methods to two existing baselines: 1) a weighted-RRT as described
by arXiv:1812.03125; 2) human demonstration seeded RRT as described by Chang
et. al. We find CA-RRT is applicable to more game maps and explores more game
states in fewer tree expansions/iterations when compared to the existing
baselines. In each test, CA-RRT reached more states on average in the same
number of iterations as weighted-RRT. In our tested environments, CA-RRT
reached the same number of states as weighted-RRT by more than 5000 fewer
iterations on average, almost a 50% reduction and applied to more scenarios
than. Moreover, as a consequence of our first person behavior cloning approach,
CA-RRT worked on unseen game maps than just seeding the RRT with human
demonstrated states.
