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Recently, GAIL framework and various variants have shown remarkablepossibilities for solving practical MDP problems. However, detailed researchesof low-level, and high-dimensional state input in this framework, such as imagesequences, has not been conducted. Furthermore, the cost function learned inthe traditional GAIL frame-work only lies on a negative range, acting as anon-penalized reward and making the agent difficult to learn the optimalpolicy. In this paper, we propose a new algorithm based on the GAIL frameworkthat includes a global encoder and the reward penalization mechanism. Theglobal encoder solves two issues that arise when applying GAIL framework tohigh-dimensional image state. Also, it is shown that the penalization mechanismprovides more adequate reward to the agent, resulting in stable performanceimprovement. Our approach's potential can be backed up by the fact that it isgenerally applicable to variants of GAIL framework. We conducted in-depthexperiments by applying our methods to various variants of the GAIL framework.And, the results proved that our method significantly improves the performanceswhen it comes to low-level and high-dimensional tasks.

Mature GAIL: Imitation Learning for Low-level and High-dimensional Input using Global Encoder and Cost Transformation