MPRL: Multiple-Periodic Reinforcement Learning for difficulty adjustment in rehabilitation games

Generally, the difficulty level of a therapeutic game is regulated manually by a therapist. However, home-based rehabilitation games require a technique for automatic difficulty adjustment. This paper proposes a personalized difficulty adjustment technique for a rehabilitation game that automatically regulates difficulty settings based on a patient's skills in real-time. To this end, ideas from reinforcement learning are used to dynamically adjust the difficulty of a game. We show that difficulty adjustment is a multiple-objective problem, in which some objectives might be evaluated at different periods. To address this problem, we propose and use Multiple-Periodic Reinforcement Learning (MPRL) that makes it possible to evaluate different objectives of difficulty adjustment in separate periods. The results of experiments show that MPRL outperforms traditional Multiple-Objective Reinforcement Learning (MORL) in terms of user satisfaction parameters as well as improving the movement skills of patients.