Boosting Exploration of Low-Dimensional Game Spaces with Stale Human Demonstrations
Kenneth Chang, Adam M. Smith · 2021 IEEE Conference on Games (CoG) · 2021
Automated game exploration methods benefit from having human demonstration data, but this kind of data is not available for each incremental build of a videogame. If we want to make use of exploration inside of continuous integration (CI) workflows, we need to leverage stale human data (from a recent version of the game). In this paper, we show how to train a goal-conditioned action policy from stale human data used in the context of RRT-based exploration of a modestly changed game version. We demonstrate the benefit of this transfer with experiments in the MiniGrid environment (which has a three-dimensional agent configuration space).