Facilitating Synchronized Movement During Ice-Breaking Scenarios Through a Real-World Reinforcement Learning Agent Using Non-Verbal Behaviors

This study investigates the application of real-world Reinforcement Learning (RL) in facilitating synchronized movement and fostering social connections during initial interactions between strangers. We introduced a novel, projection-based system where an RL agent is projected onto a table to interact with participants through its movements. Our research involved the development and testing of a Deep Q-Network (DQN) integrated with a Long Short-Term Memory (LSTM) model that identifies synchronized movements and determines the reward for the DQN. The preliminary results showcase the effectiveness of the LSTM model, revealing that synchronized movement can be a potent reward mechanism, enabling RL agents to learn effective policies. The initial findings indicate the feasibility of the RL approach in enhancing social interactions, providing partial support for our designed actions.