Effective decision-making is a key challenge in artificial intelligence, with Reinforcement Learning (RL) emerging as one of the main approaches.However, RL often depends on complex reward functions, which are difficult to design.Intrinsic motivation, inspired by psychological concepts like curiosity, offers an alternative by generating agent-driven rewards to foster exploration.This paper introduces intrinsic motivation into the Elastic Decision Transformer (EDT) framework for Offline RL.By using an auxiliary intrinsic loss, we enhance representation learning without altering fixed reward signals.Experiments in locomotion tasks demonstrate improved performance, underscoring the potential of intrinsic motivation to advance RL in offline settings.

In an era characterized by rapid technological advancement and rising customer expectations, accurate ticket classification in banking customer service emerges as a critical necessity. In this context, we designed a comprehensive ticket classification pipeline, leveraging a real-world dataset comprising 4,243 chat-based user requests, classified into ten distinct classes, provided by MPS Bank. Our approach proposes a complete data processing pipeline with an exploration of two text classification methodologies: BERT (Bidirectional Encoder Representations from Transformers) and TF-IDF (Term Frequency - Inverse Document Frequency) with SVM (Support Vector Machine). The experiments highlight that both models have considerable potential, promising substantial improvements in the operational efficiency of customer support, ultimately increasing the overall quality of service.