Exploring Q-Learning for Adaptive Difficulty in a Tile-based Image Labeling Game

Participant disengagement in citizen science tasks remains a significant challenge for crowdsourcing platform creators, in their efforts to generate meaningful data and connect with their membership. Reinforcement learning is increasingly used to take advantage of the plethora of available data to learn to sequence tasks for participants. To this end, we extend the reinforcement learning techniques used in Tile-o-Scope Grid, an image matching web game, by introducing an adaptive Q-learning based approach that incorporates participant performance in sequencing the difficulty of levels. We compared our adaptive version against both a previous non-adaptive algorithm, as well as a greedy approach. We found that the adaptive extension outperformed both, in terms of total reward. This work contributes to the growing literature on reinforcement learning approaches applied to citizen science.