  Deep Reinforcement Learning has shown tremendous success in solving several
games and tasks in robotics. However, unlike humans, it generally requires a
lot of training instances. Trajectories imitating to solve the task at hand can
help to increase sample-efficiency of deep RL methods. In this paper, we
present a simple approach to use such trajectories, applied to the challenging
Ball-in-Maze Games, recently introduced in the literature. We show that in
spite of not using human-generated trajectories and just using the simulator as
a model to generate a limited number of trajectories, we can get a speed-up of
about 2-3x in the learning process. We also discuss some challenges we observed
while using trajectory-based learning for very sparse reward functions.
