  Recently, GAIL framework and various variants have shown remarkable
possibilities for solving practical MDP problems. However, detailed researches
of low-level, and high-dimensional state input in this framework, such as image
sequences, has not been conducted. Furthermore, the cost function learned in
the traditional GAIL frame-work only lies on a negative range, acting as a
non-penalized reward and making the agent difficult to learn the optimal
policy. In this paper, we propose a new algorithm based on the GAIL framework
that includes a global encoder and the reward penalization mechanism. The
global encoder solves two issues that arise when applying GAIL framework to
high-dimensional image state. Also, it is shown that the penalization mechanism
provides more adequate reward to the agent, resulting in stable performance
improvement. Our approach's potential can be backed up by the fact that it is
generally applicable to variants of GAIL framework. We conducted in-depth
experiments by applying our methods to various variants of the GAIL framework.
And, the results proved that our method significantly improves the performances
when it comes to low-level and high-dimensional tasks.
