Dynamic Difficulty Adjustment Using Reinforcement Learning for Adaptive Gameplay Experience in Resik - Diny Syarifah Sany; M Malwan Angkasa (Universitas Suryakancana Cianjur, 2025)
The development of educational games holds significant potential for interactively instilling environmental conservation concepts, such as reduce, reuse, and recycle (3R). However, varying player skill levels often lead to boredom when a game is too easy, or frustration when it is too difficult. Although Reinforcement Learning (RL)-based Dynamic Difficulty Adjustment (DDA) has proven effective in balancing difficulty levels in action or MOBA genres, its application in educational waste management games remains underexplored. This study aims to develop an educational game titled