  In this study, we enhance the Maximum Entropy Deep Inverse Reinforcement
Learning (MEDIRL) framework, targeting its application in human robot
interaction (HRI) for modeling pedestrian behavior in crowded environments. Our
work is grounded in the pioneering research by Fahad, Chen, and Guo, and aims
to elevate MEDIRL's efficacy in real world HRI settings. We replicated the
original MEDIRL model and conducted detailed ablation studies, focusing on key
model components like learning rates, state dimensions, and network layers. Our
findings reveal the effectiveness of a two dimensional state representation
over three dimensional approach, significantly improving model accuracy for
pedestrian behavior prediction in HRI scenarios. These results not only
demonstrate MEDIRL's enhanced performance but also offer valuable insights for
future HRI system development, emphasizing the importance of model
customization to specific environmental contexts. Our research contributes to
advancing the field of socially intelligent navigation systems, promoting more
intuitive and safer human robot interactions.
