Unconventional formats of background knowledge from human teacher in reward shaping

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Conference Proceeding
ACM/IEEE International Conference on Human-Robot Interaction, 2017, pp. 373 - 374
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© 2017 Authors. This research evaluates reward shaping with unconventional formats of human input. Conventionally, a human teacher is assumed to provide numeric rewards for complete task training. However, there are limitations to this conventional format. Firstly, the continuous demand of numeric rewards is onerous. Secondly, it is limited in extracting useful knowledge from humans. In this research, we have tested three unconventional formats of human input, two to increase social appeal of reward shaping and one to efficiently extraction knowledge from humans. The preliminary results on simulated domains validate the usefulness of these formats in terms of user's comfort and learning performance.
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