The material delivery route prediction problem aims to forecast the future delivery routes of couriers given a set of tasks. Due to the high non-linearity and complexity of vast historical data, as well as factors related to individual courier preferences, this task poses significant challenges. Most existing methods use deep neural networks based on supervised learning to capture behavior patterns from historical data. However, they often struggle with the dynamic nature of the data and the diversity of individual preferences. This paper proposes a new deep reinforcement learning framework that integrates Variational Autoencoders (VAE) to handle large-scale data features and incorporates dynamic embedding features to accurately reflect personal preferences. The framework is trained using Proximal Policy Optimization (PPO) for optimized policy. Experimental validation with two publicly available real-world urban delivery datasets from Cainiao Network and two datasets generated from cities across multiple countries shows that the proposed framework significantly outperforms seven existing prediction methods. 

Zero-Shot object Detection (ZSD), one of the most challenging problems in the field of object detection, aims to accurately identify new categories that are not encountered during training. Recent advancements in deep learning and increased computational power have led to significant improvements in object detection systems, achieving high recognition accuracy on benchmark datasets. However, these systems remain limited in real-world applications due to the scarcity of labeled training samples, making it difficult to detect unseen classes. To address this, researchers have explored various approaches, yielding promising progress. This article provides a comprehensive review of the current state of ZSD, distinguishing four related methods—zero-shot, open-vocabulary, open-set, and open-world approaches—based on task objectives and data usage. We highlight representative methods, discuss the technical challenges within each framework, and summarize the commonly used evaluation metrics, benchmark datasets, and experimental results. Our review aims to offer readers a clear overview of the latest developments and performance trends in ZSD.