Experiments for Reinforcement Learning with Curiosity-driven Exploration

Abstract

In recent years, reinforcement learning motivated by curiosity has attracted attention. By learning features related to agents' behavior, they can focus on changes in things that are interesting from images, without being distracted by screen noise or meaningless changes. In addition, it has been reported that even with the learning result based only on the internal compensation, it has the generalization ability to be effective even at the stage other than the stage used for learning. However, in the reference paper, it was only applied to 2 stages in the "Super Mario Bros." Therefore, to further investigate the generalization ability of reinforcement learning motivated by curiosity, I experimented on more stages. As a result, I confirmed the generalization ability of reinforcement learning motivated by curiosity.