Comparative Analysis of Model-Based and Model-Free Reinforcement Learning for UI Adaptation EICS007

User interface (UI) adaptation plays a crucial role in enhancing user experience by dynamically adjusting to individual preferences and situational needs. This study presents a comparative analysis of Model-Based Reinforcement Learning (MBRL) and Model-Free Reinforcement Learning (MFRL) as intelligent controllers of UI adaptations. We build upon an implementation of MFRL for emotion-driven UI adaptation and integrate an MBRL approach using Monte Carlo Tree Search (MCTS) with Upper Confidence Trees (UCT) for decision planning, along with a neural network for simulating user emotion responses. Experiments were conducted in a mobile EvacuationApp designed for emergency training, where participants interacted with adaptive UIs driven by either MBRL or MFRL. Our findings indicate that MBRL excels in scenarios with predictable user behavior, where a model of the interaction can be learned, whereas MFRL is more effective in dynamic, uncertain environments with frequent changes in user behavior. In our user study, MFRL achieved superior overall performance in eliciting the intended emotional responses, notably increasing users’ happiness level over successive adaptation cycles. This work offers a deep understanding of reinforcement learning-based UI adaptation strategies, examining the benefits and limitations of each approach.