Learning Nudge: Affect-Aware Model-Free Reinforcement Learning is a M.Tech project topic for Electrical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Learning Nudge: Affect-Aware Model-Free Reinforcement Learning Project Details
| Abstract |
This research explores an affect-aware, model-free reinforcement learning framework designed for personalized energy-feedback nudging, addressing the limitations of static digital energy feedback systems that often overlook users’ emotional states. The proposed system integrates an Affect–Behavior Decoupling Architecture (ABDA) within a classical MAPE-K loop, enabling parallel processing of emotional signals, derived from real-time facial emotion recognition, and behavioral cues. A reinforcement learning agent is developed to autonomously determine optimal nudging strategies, learning when, how, and which interventions to deliver based on the user’s detected affective state and historical interaction data, thereby tailoring interventions without explicit pre-programmed rules. The application context is household energy conservation. Empirical validation was conducted through a simulated smart-home dashboard and a controlled laboratory study. Results indicate that emotionally adaptive feedback significantly enhanced positive affect and sustained user engagement compared to a non-adaptive baseline. Furthermore, the adaptive condition increased exposure to conservation-relevant cues and contributed to modest improvements in self-reported energy awareness. This work contributes an ABDA–MAPE-K design for hybrid affective-behavioral adaptation and a model-free reinforcement learning agent capable of discovering personalized nudge policies from data. |
| Reference Paper |
Learning to Nudge:Affect-Aware Model-Free Reinforcement Learning for Energy Conservation |
| Domain |
Electrical Engineering |
| Sub-Domain |
Control Systems / Adaptive Control |
| PDF Download |
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| Get Help |
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